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Musings of an Energy Nerd

Energy Modeling Isn’t Very Accurate

Before spending time or money on energy modeling, it’s important to know its limitations

Complicated computer models are less accurate than simple ones. A study sponsored by the Energy Trust of Oregon compared the accuracy of four energy software programs. Surprisingly, Michael Blasnik's Simple spreadsheet proved to be more accurate than models that required far more inputs.
Image Credit: Table and graph from Michael Blasnik; window calculation formula from Bronwyn Barry

Energy consultants and auditors use energy modeling software for a variety of purposes, including rating the performance of an existing house, calculating the effect of energy retrofit measures, estimating the energy use of a new home, and determining the size of new heating and cooling equipment. According to most experts, the time and expense spent on energy modeling is an excellent investment, because it leads to better decisions than those made by contractors who use rules of thumb.

Yet Michael Blasnik, an energy consultant in Boston, has a surprisingly different take on energy modeling. According to Blasnik, most modeling programs aren’t very accurate, especially for older buildings. Unfortunately, existing models usually aren’t revised or improved, even when utility bills from existing houses reveal systematic errors in the models.

Most energy models require too many inputs, many of which don’t improve the accuracy of the model, and energy modeling often takes up time that would be better spent on more worthwhile activities. Blasnik presented data to support these conclusions on March 8, 2012, at the NESEA-sponsored Building Energy 12 conference in Boston.

Blasnik sees more data in a day than most raters do in a lifetime

Blasnik has worked as a consultant for utilities and energy-efficiency programs all over the country. “I bought one of the first blower doors on the market,” Blasnik said. “I’ve been trying to find out how to save energy in houses for about 30 years. I’ve spent a lot of time looking at energy bills, and comparing bills before and after retrofit work is done. I’ve looked at a lot of data. Retrofit programs are instructive, because they show how the models perform.”

According to Blasnik, most energy models do a poor job of predicting actual energy use, especially for older houses. And since…

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  1. wjrobinson | | #1

    30 years ago Bruce Brownell
    30 years ago Bruce Brownell started designing and building homes that finally this article, Blasnik and others say we all should be building now.

    Continous insulation
    Outsulate to keep the frame warm and minimize moisture issues
    Less bumpouts
    Insulate all 6 sides of a building continuously
    Solar orientation
    Use PV

    Bruce Brownell Adirondack Alternative Energy

    Congratulations Bruce for the success you have shared with us for so many years!

  2. wjrobinson | | #2

    When will Energy Modeling be open source????
    We need to get with the times and stop locking up useful tools!@! Michael, meet my challenge. Build a GBA sponsered FREE FOR ANYONE or at least PRO members spreadsheet energy modeling program.

    The time is now for this site to have ENERGY MODELING.

    Build and publish this GBA and you will see your readership DOUBLE!!!!!!!!!!!!!!!!!!

    Dan.... are you paying attention my man.... Soup for you if you are!

  3. jklingel | | #3

    I'm glad this topic was
    I'm glad this topic was raised. I have always wondered if all the complexity of these super-programs was just an exercise in trivia; diddling w/ the small variables that really don't amount to much and can be hard, if not impossible, to model. Very interesting report.

  4. user-963341 | | #4

    What's the replacement?
    I have done a lot of modeling over the years. My primary tool lately has been TREAT, although I use REM and EQUEST as well. Bottom line, I largely agree with Michael, modeling is not accurate enough to justify the time we spend with it and the decisions made from it.
    Why is it so well accepted? Well, in the first place people like to make spending decisions based on facts, whether the facts are true or not, CYA. For efficiency programs modeling is often the basis of their justification to exist. And in either case if someone else's money is involved the need for due diligence must be met somehow.
    The question remains, if we stop modeling (which wouldn't break my heart) how do we satisfy those needs? Do we need to do more accurate modeling or should we get people used to accepting a little uncertainty about ROI?
    I don't think we will eliminate the demand for modeling for the CYA reasons mentioned above. It's too nice to have a number, any number, that someone spent a lot of time and energy to derive. I do think it would be great to break that dependency, I just don't think it will happen anytime soon.
    Of course the other side of that is us, the modelers. It's a kind of satisfying geeky thing to do. And it gives us an answer, and we like that too. So there is a lot of work to do to break this co-dependency, thanks Michael (and Martin) for putting this out there.

  5. user-831308 | | #5

    And why are we doing all this in the first place?
    I totally appreciate this article. As a person who works for a company which pays me to help homeowners (remodel/add on/simply improve) their existing homes, my employer and I are fully aware that improving the efficiency of a house is just one on a long list of things the average person might potentially want to do to their house, assuming they can afford to do anything. And as a green remodeler, energy efficiency must be weighed with equal respect to resource efficiency, longevity, and health.

    I hate to say it, but efficiency improvement is on the "non-sexy" list almost all time. Perhaps that's why window replacement companies have more success than the energy auditors and insulators. At least one can "touch and feel" their new windows (wink, wink), even if they may never recoup the cost of them.
    Moreover, how many quadratic equations "should" it take to persuade a person that caulking that window may save them money in the long run. Sometimes the answer is in the form of a different question.


  6. bXKM8kAK9j | | #6

    Alternatives to Modeling
    We could always go back to this marketing pitch:
    "_______ (fill in the blank) will save you (30%, 40%, 50%) on your energy bills."

    Every prediction of energy savings is based on some kind of calculation. Modeling with an interactive UA calculation algorithm that considers interactive measures, and is based on tons of research, has broader credibility than some contractor's spreadsheet where we don't know what the calculations are based on.

    Though it may not be spot-on, modeling allows many incentive programs a basis for providing much-needed funding at time of retrofit, rather than waiting a year for energy bills to prove the homeowner is eligible for the incentive. Incidentally Suzanne Shelton of The Shelton Group presented research at ACI National this week that 64% of homeowners who did 1-3 energy improvements ended up using more energy. Customers who received retrofits felt they did not have to be as careful with consumption afterward.

    Inaccurate assumptions of the efficiency of existing features indicates there is insufficient guidance for software users in how to evaluate these things. Perhaps software providers, and the industry, could improve guidance on the evaluation of older systems.

    Manual J is an energy model too, and though it also has its critics it seems to be generally accepted as the best method we have for sizing HVAC systems. I don't think we want to replace it with the method where you stand on the curb across the street and hold up the cardboard cutout to see what size unit the house needs.

  7. DavidButler | | #7

    modeling is just one of many factors...
    Thank you for writing this article, Martin. Finally, the truth about energy modeling. Although I rely on modeling tools in my new construction work, especially for design loads, the results are only a starting point, one input among several factors that inform my design recommendations.

    Only by understanding the limitations and weaknesses of a tool can we properly interpret the results. This type of judgment is not something that can be learned in class or a book, but rather through experience, curiosity, and feedback.

    Efficiency programs that rely solely on modeling results are deluding themselves. For this reason, I did not support Home Star, and do not support the proposed 2012 HOMES Act.

    I do take issue with your characterization of not needing to use test equipment when sealing ducts. While I agree in the case of duct leakage testing, it's important to test system airflow. Otherwise, a newly tightened duct system could cause airflow to drop below acceptable limits, even leading to coil freeze-up. Manclark was one of the first to write about this in the May 2001 edition of Home Energy Magazine. Fortunately, airflow testing doesn't take as long as prepping for a duct leakage test, but it does require significant training.

  8. GBA Editor
    Martin Holladay | | #8

    Response to Joe Blowe
    You reported that "64% of homeowners who did 1-3 energy improvements ended up using more energy." Now that's an interesting finding!

    You wrote, "Inaccurate assumptions of the efficiency of existing features indicates there is insufficient guidance for software users in how to evaluate these things." Possibly -- but I tend to side with Blasnik in his conclusion that these types of systematic errors are due to bad software algorithms and bad default assumptions. If the software developers bothered to compare their models to the utility bills of occupied homes, then the software could be improved and corrected.

    You wrote, "Manual J is an energy model too, and though it also has its critics it seems to be generally accepted as the best method we have for sizing HVAC systems. I don't think we want to replace it with the method where you stand on the curb across the street and hold up the cardboard cutout to see what size unit the house needs." Actually, Blasnik made a cogent argument to the effect that rules of thumb make more sense than Manual J calculations.

    I hope that Michael will comment here on the Manual J question, but here are some notes I made of his opinions on Manual J. "It's bizarre how Manual J is treated as if it had been carved in a stone tablet, when actually it is a really crude and simplified model. It has all these fudge factors. And this precision doesn't really matter, because they don’t make heating and cooling equipment in all these increments. If you are sizing residential cooling equipment, there are only 5 possible answers. Bruce Manclark has worked on developing a simplified version of Manual J and Manual D, with fewer inputs. For 90% of most AC installations, sizing per square foot actually works pretty well. We just need better rules of thumb."

  9. GBA Editor
    Martin Holladay | | #9

    Response to David Butler
    You point about testing system airflow is an interesting one; like many writers on energy topics, I have been advising people of the importance of testing system airflow for many years. I'm interested in hearing Michael Blasnik's response to your concern that sealing duct leaks without testing system airflow might lead to problems.

  10. Ken Levenson | | #10

    Regarding the PHPP
    While a simple energy efficiency retrofit may not merit a rigorous analysis – a gut renovation and new construction should.

    Wolfgang Feist has recently tweeted, “results are in - consumption in Passive Houses really as low as predicted - to be presented at the conference”
    I encourage everyone to attend the Passive House conference in Hanover Germany in May, and/or seek out the presentation online when made available.

    An essential element of Passive House and the utilization of the PHPP is not to reduce energy use for heating and cooling for the sake of it, but as a serious attempt to help mitigate the worst effects of climate change. Given a retrofit or new building project, achieving anything less than what we know is readily achievable via the Passive House standard, should be seen as a lost opportunity in our climate mitigation efforts. The PHPP allows us to predictably make the most of our mitigation efforts.

    So, if a fundamental goal in optimization is, as it is with Passive House, an approximate 90% reduction in heating and cooling loads, allowing for a proportional reduction in a heating/cooling plant – one needs to be confident in the numbers! At 4.75 kbtus/sf/hr (the PH standard) a 25% swing is only 1.2 kbtus. A typical Energy Star Home may have a heat demand of say, 65 kbtus/sf/yr, no? making a 25% swing for the Energy Star Home16 kbtus - or over three times the entire PH demand. So if one is looking for Passive House optimization, the PHPP is required. It is not a luxury.

    Thermal bridging is the elephant in the room. HERS, Energy Star may address thermal bridging but unless the bridging loses are actually calculated and put in to the model – the model is destined to be 20% off from the get go. The most pernicious thermal bridge, the installed window frame, is only seriously addressed by the Passive House program, which essentially requires the overinsulation of all window and door frames – and not coincidentally, addressing a common problem spot of condensation issues. Without serious attention to thermal bridging calculations – the models will never be great and can be most problematic for Passive House level construction.

    It seems to me, the 4.75 kbtu/sf/yr threshold is too often a distraction. Yes, if one wants to identify a building as a CERTIFIED PASSIVE HOUSE, there are very strict definitional requirements. You hit it or you don’t, you are in or out, clean and clear.

    But Certification should be seen as a key quality control and value added component. The PHPP is complex and the numbers matter. The Certification process allows for proper checking of component values and system integration - by the PHI accredited certifier and PHI itself. Practitioners learn a great deal in the Certification back-and-forth process, and so Certification is highly recommended, especially for a practitioner's first few projects.

    We need to be clear as well that the 4.75kbtu annual heat demand and 0.60 airtightness are the MINIMUM requirements for Certification, the goals are really a third and half again better respectively. If you can't hit the minimum requirements for Certification in a new building PHI doesn't think you're really trying all that hard.

    But if you don’t want the certification, and/or specific project constraints make hitting the strict goals unattainable, YET the project uses Passive House components and uses the PHPP and the methodology etc… one can legitimately call the project a Passive House Project – it is just not a certified project and PHI will not vouch for its performance as such. If instead of 4.75 kbtus/sf/yr your building is at 6 or 7 kbtus/sf/yr, you still likely have an awesomely efficient building - just not one that can be identified as a Certified Passive House.

    Yes, plug loads are a problem, not just in energy use but in proper functioning of the systems, particularly at Passive House levels, and must be addressed. But they hardly seem an excuse to not make the basic building enclosure as good as the PHPP can help you make it.

    Regarding the complexity and training required for the PHPP – for the uninitiated it is a nightmare to be sure. But with the investment in training and the first few buildings, one typically sees the hours required to complete the PHPP dramatically drop and become quite speedy and manageable. If one is interested in training in PHPP or the all important thermal bridge calculations I recommend you contact the Passive House Academy ( . I’ve taken their thermal bridging class and it is amazing – top notch instruction. And there are growing numbers of Certified Passive House Consultants (CPHCs), who are happy to help you with the calculations. Regional groups such as Passive House Maine, Passive House New England, Passive House New York, Passive House Northwest and Passive House California can put you in touch with them.

  11. GBA Editor
    Martin Holladay | | #11

    Response to Ken Levenson
    I'll do my best to respond to some of your comments, but because you raised so many points, I may be unable to respond to everything in depth.

    Lowering a building's space heating needs to Passivhaus levels is a worthwhile goal, but I disagree with the assertion that it matters very much if you miss the target. As my recent article on occupant behavior shows, building a Passivhaus doesn't guarantee low energy bills. The occupants can still use too much energy, even in a Passivhaus.

    You wrote, "HERS, Energy Star may address thermal bridging but unless the bridging loses are actually calculated and put in to the model – the model is destined to be 20% off from the get go." So what? Much bigger swings than 20% of the heat requirement of a Passivhaus are possible due to thermostat settings, domestic hot water usage, or electronic appliance usage. Energy modeling precision is a myth.

    I disagree with your assertion that "the Passive House program ... essentially requires the overinsulation of all window and door frames." I've reported on many Passivhaus buildings over the years, and visited several, and many of them have achieved the Passivhaus standard without overinsulating the window frames on the exterior. However, anyone who wants to save a few more BTUs a year is free to overinsulate their exterior window frames if they want -- it's a good technique for some window types, as long as the cost to execute this detail are proportional to the (tiny) savings.

    Mitigating the effects of greenhouse gas emissions and addressing climate change is, indeed, a noble goal and a huge challenge. But building more new single-family homes in the U.S. (Passivhaus or not) is not the solution. The solution includes the elimination of coal-burning power plants, a radical rethinking of our transportation infrastructure, a series of cost-effective retrofit measures on existing buildings, and steep new taxes on fossil fuels.

    There is no way we will build our way out of the current climate crisis with Passivhaus contruction methods.

  12. user-659915 | | #12

    Response to Ken Levenson

    Given a retrofit or new building project, achieving anything less than what we know is readily achievable via the Passive House standard, should be seen as a lost opportunity in our climate mitigation efforts. The PHPP allows us to predictably make the most of our mitigation efforts.

    While I think all of us agree that dramatic reductions in residential heating and cooling loads are both possible and important in the fight against global warming I'd suggest we be alert to the tunnel vision that can result from an entirely singular focus in that struggle. I beat the gong once again for the importance of opportunity cost within a more complex overall calculus: assuming a zero-sum total financial resource we might for example consider it more important to spend rather more on an in-town lot to increase walkability and reduce vehicle emissions and rather less on massive insulation, especially in more moderate climates. According to the perspective introduced here, "what we know is readily achievable" - in that more integrated view - may be better aimed at a level which does not require complex calculation.

    This perspective also allows the very real advantage that many more designers, builders and, most important, homeowners may be brought into the fold. To pluck numbers out of the air in a vastly imprecise but possibly somewhat accurate estimate of the potential, 75% reduction in the energy use of 50% of the housing stock is going to do vastly more good than a 90% reduction for the favored few who can provide the resources to incur PH's substantial cost. For those of us who choose or are compelled to work in that less rarefied air simple but reliable rules of thumb are an essential part of the toolkit. Thanks Martin for this validation.

  13. Ken Levenson | | #13

    response to Martin and James
    Clearly agreed - building efficiency is but one tool in the box for mitigating climate change. World War II proportion effort is required, demonstrated, among other places by climate wedges here:
    We can't use the excuse of other required efforts (such as more multi-family and better urban planning) to dismiss what is reasonably possible in building efficiency alone.

    Passive House is approaching cost parity in Europe and with growing adoption in US there is no reason to think it can't here too.

    I quite agree, and stated so, Martin: That hitting the exact number only matters if you want Certification. Ideally we should be trying to do better than the requirement but coming up short is not a mortal sin.

    When the loads get tiny the thermal bridging matters a great deal.

    I must admit that the general protestations against the rigors of Passive House, to me, echo those against the rigors of Japanese automobiles in the 1970s.....and we know how that turned out.

    Wolfgang Feist seems to be disagreeing about the predictability of the PHPP. It will be interesting to see the presentation.

    Enjoying the dialog.

  14. user-723121 | | #14

    Start with energy bills
    In planning for an energy retrofit for our house in Minneapolis energy bills for the previous 15 months were obtained from the utility provider. I used an Excel spreadsheet with15 inputs and assigned square footages and U values for the building elements of the house. The heat loss calculations were done manually using heating degree days, this was compared to
    normalized energy use from the utility billing data.

    With this information a retrofit priority list was made, air sealing, attic insulation, foundation insulation and a 95% furnace were the 4 improvements made. The envelope improvements were made first and the old furnace was used for an additional heating season. With this information I sized the new furnace based on normalized energy use and assumed efficiency of the old (1978) forced air furnace @ 65%.

    I modeled the post retrofit house with both Rem Design and Energy 10 and found them to be in agreement as to the predicted energy use. The actual energy use was very close to the models, within 5%. The house pre retrofit used 4.06 Btu/sf/hdd and the post retrofit usage is as follows,

    2007 2.1 Btu/sf/hdd
    2008 2.27
    2009 2.4
    2010 2.38
    2011 2.28

    We setback twice daily in 2007, at night and then during the weekday, this may account for the lower energy consumption for that year.

    I believe energy models can be used quite accurately on energy efficient homes, this seemed to be true when modeling superinsulated homes in the 1980's. The energy modeling software was not very sophisticated back then, maybe that's why it worked.

  15. GBA Editor
    Martin Holladay | | #15

    Another response to Ken Levenson
    You wrote, "I must admit that the general protestations against the rigors of Passive House, to me, echo those against the rigors of Japanese automobiles in the 1970s."

    I'm not protesting against the program's rigors; I'm just questioning whether the hours spent modeling are hours well spent. I'm also questioning whether the high cost of some new construction measures can be justified by the anticipated energy savings.

    You wrote, "Wolfgang Feist seems to be disagreeing about the predictability of the PHPP."

    I'm happy to stipulate that it's possible that energy monitoring data from 1,000 buildings in Europe may show that the average energy use data correlates well with PHPP projections. That doesn't change the fact that: (a) there will still be a bell curve distribution, with low-use households on one end and high-use households on the other, and that (b) domestic hot water use and plug loads are the most important factors explaining this bell curve.

    Finally, I suspect that monitoring of Passivhaus buildings in the U.S. will likely reveal that, on average, U.S. families use more domestic hot water and have higher plug loads than the default PHPP values developed for German families.

  16. user-659915 | | #16

    Response to Ken

    Passive House is approaching cost parity in Europe and with growing adoption in US there is no reason to think it can't here too.

    Actually there are couple of reasons why that's unlikely to happen here, the typically much greater size and complexity of US homes for one and the much lower price of energy for another being barriers to widespread adoption. But I assume that your comment is focused on new construction which with the current huge inventory of unsold homes is hardly the greatest of our problems right now, and no way is 'cost parity' a consideration with renovation of existing homes. Getting clients to spend 20K - 40K out of a 200K renovation budget on energy upgrades is a struggle, a 100k deep energy retrofit is next to impossible, and the jump beyond that to a PH level upgrade is simply a bridge too far for all but the very rarest of energy-focused and deep-pocketed homeowners.

    By the way, I've been telling my clients for two decades that energy prices are bound to rise dramatically sooner or later and they should regard ROI in that context. This is beginning to get old for all of us I think and my spiel has changed. Forget about ROI - the reason for including energy upgrades in your budget is simply because it's the right thing to do. What, after all, is the ROI on all the other discretionary costs in home construction, whether it's trendy recycled glass countertops or sustainably harvested cork and bamboo floors?

  17. Ken Levenson | | #17

    in response to James
    I quite agree regarding ROI as far as the "selling" goes... I've ask clients: what was the ROI on your vacation to Mexico? or how about the fancy restaurant dinner? Or your nice car? So I don't even bother selling Passive House based on ROI but based on comfort and health.....and yeah, by the will pay for itself, and you'll be doing good for your kids and grandkids. It becomes rather silly not to do it, imho - because at that point all that is stopping them is the desire for a wood burning fireplace and high output gas stove.... And yes, I'm talking about first adopters with relatively more money to spend.....they are the first adopters after all. But will greater numbers, the costs will go down and the client base will broaden significantly. Habitat for Humanity is one of the biggest home builders in America and seems to be significantly moving in the Passive House direction.

    Regarding renovation assumes only that a renovation is going to be a gut renovation to begin with.

    As for new construction, there will be something on the order of 700,000 new housing unit starts this year.....plenty of units to consider the implications of 25% better vs. 75% etc....and their use over the next 50 years.

  18. Ken Levenson | | #18

    in response to Martin

    Life is a bell curve - it is not a disqualifying characteristic. And the idea that the outliers in the PHPP bell curve are likely driven by plug loads and DHW only reinforces the validity of the PHPP to accurately predict the performance of the modeled enclosure. No?

    And agreed, the plug load and DHW assumptions are off for US usage. Regarding both, there is no get the most accurate results one should adjust values from default numbers to reflect likely usage. And for properly sizing various systems, it is important to do so.

  19. wjrobinson | | #19

    Ken, wrong; life is not a bell curve
    Much that is studied is actually NOT, on a bell curve...

    Study a bit more thoroughly... Ken

  20. Ken Levenson | | #20

    aj're kidding, right?
    My comment regarding life as a bell curve was a gross over generalization...I thought, obviously enough.

    Wikipedia on bell curves:

    In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function, known as the Gaussian function or informally the bell curve:[nb 1]

    where parameter μ is the mean or expectation (location of the peak) and is the variance. σ is known as the standard deviation. The distribution with μ = 0 and σ 2 = 1 is called the standard normal distribution or the unit normal distribution. A normal distribution is often used as a first approximation to describe real-valued random variables that cluster around a single mean value.
    The normal distribution is considered the most prominent probability distribution in statistics. There are several reasons for this:[1] First, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. Second, the normal distribution arises as the outcome of the central limit theorem, which states that under mild conditions the sum of a large number of random variables is distributed approximately normally. Finally, the "bell" shape of the normal distribution makes it a convenient choice for modelling a large variety of random variables encountered in practice.

    But I appreciate your concern for my mathematical education - I'll take it up with my statistician father-in-law. All best. ;)

  21. GBA Editor
    Martin Holladay | | #21

    Response to Ken
    I don't quite see how you can sell your clients on Passivhaus based on "comfort and health.....and yeah, by the will pay for itself." I think that it's perfectly possible to build an extremely comfortable, healthy house without meeting the Passivhaus standard -- and I think than it many climates in the U.S., the level of insulation required to achieve the Passivhaus standard will never pay for itself.

    I never said that "outliers in the PHPP bell curve are likely driven by plug loads." I'm saying the families in the dead center of the bell curve will have energy use profiles that are driven by plug loads.

    If you agree that "the plug load and DHW assumptions are off for US usage," your statement implies that the oft-repeated claim that a Passivhaus will use only 10% of the energy of a "normal" house is untrue. Higher domestic hot water use and higher plug loads (typically of North American families) make that statement highly unlikely.

    Ken, I agree with you that "life is a bell curve" -- I knew what you meant. But what we all need to think about (when we consider the two families I profiled in an earlier blog, the low-use family who lived in a net-zero house and the high-use family living in a Passivhaus -- you'll remember that the Passivhaus family used 7 times as much electricity as the net-zero family) is: Which levers will be most effective at moving the bell curve to the left?

  22. Ken Levenson | | #22

    in response to Martin
    The 90% reduction claim applies only to heating and cooling annual demand - a fundamental distinction.
    If the occupants "behave" one might expect an overall 75% energy usage drop.

    Because as designers we can "control" the enclosure and basic systems, as opposed to the plug loads and how long a shower the occupants take, I'm inclined to apply the most rigor to the enclosure, and then with more efficient electronics and hot water heating systems, and more conscientious habits we can drive down the others.
    (btw: i still "yell" at my wife to turn out the lights when she leaves a room......tough habits to break.)

  23. GBA Editor
    Martin Holladay | | #23

    Response to Ken
    You wrote, "The 90% reduction claim applies only to heating and cooling annual demand."

    Oh, if only you were right! Sadly, you're wrong. Passivhaus advocates often repeat the falsehood that a Passivhaus building uses 90% less energy than a "normal" house.

    I found these examples on the Web in no time at all:

    Passive House DC: "Passive Houses save 90% of household energy."

    Passive House Alliance: "Passive House: no boiler, no furnace, highest comfort and up to 90% less energy."

    One Sky Homes: "Reduce energy consumption by 90% and enjoy amazing comfort with a Passive House."

    Passive House and Home: "A passive house is 90% more efficient than a standard house."

    TE Studio: "Passive House aims to reduce energy in buildings by up to 90% while providing superior comfort and indoor environmental quality."

    Clarum Homes: "A passive home is an extremely comfortable, healthy, economical, and sustainable home, designed and constructed to use up to 90% less energy than a traditional home."

    Sadly, such exaggerations are the rule, not the exception.

  24. Ken Levenson | | #24

    response to Martin
    Yes, I completely agree that there is a communication problem. Every time I see such a claim - I want to insert (space heating and cooling) in front of "energy".

    However if you look at the standard, 4.75kbtus/sf/yr space heating demand translates into roughly a 90% reduction from "typical heating demand" in heating climates. So I'm right so far as what the standard actually is supposed to achieve. and another statistical average. ;)

    To get it straight from the Germans, see here:

    Measured results have shown that the actual heating consumption values of such houses are about 90% less than the consumption values of ordinary existing buildings in Germany.

    I should add that PHI has been inconsistent in their I see right above my quote link a reference only to energy and not heating....their inconsistency has led others to overstate it would seem.....

  25. GBA Editor
    Martin Holladay | | #25

    One other basis for the exaggeration
    The other way that this exaggeration overstates the case for Passivhaus is that it compares a Passivhaus building to "ordinary existing buildings in Germany" rather than code-minimum buildings. Obviously, Germany has a lot of very old buildings, many of which are hundreds of years old, including lots of buildings with uninsulated walls.

    In the U.S., residential energy codes have been ramping up. A more realistic comparison for new buildings in the U.S. is to compare a Passivhaus builiding with a code-minimum U.S. building, not an "existing building in Germany."

  26. Ken Levenson | | #26

    regarding PH vs. code minimum
    Fair. But the implication as far as I've understood it, has only been relative to existing building stock.
    I'll find out info regarding PH vs. code minimum and report back.

    On a side but related note: while ROI is always a factor and part of the equation to ensure affordability, the goal of the claim, as I see it, is one geared not toward the economics per se, but our efforts toward the mitigation of global warming.

  27. AndyKosick | | #27

    Couldn't agree more. New Idea?
    This article may be just what I need to hear to change the way I do energy audits. I've been a remodeler my whole life and doing energy retrofits full time for 3 years now and have been frustrated with energy models for 2 of those years. I figured out right away they rarely agree with the UT bills and I usually find myself figuring out the problems and talking to the customer too much and not having time to collect all the data. I'm all for measuring results but modeling is wearing thin.

    Also (because I'm not a computer programer), I'd like to advocate for a new kind of software for existing homes (maybe it exists) that scraps the model and uses simple parameters like square footage, number of stories, foundation type to predict saving based on the averages of actual measured savings of a given improvement on similar houses. I'm talking huge database that is continuously updating. I'd pay for a subscription. I can't be the first person to have thought of this can I??? Some UT bill disaggregation maybe necessary to pull out occupant behavior but I can't think of anything more simple and more accurate for retrofits. Needless to say I'm compiling my own.

    As for PHPP, I love the concept but have often mused of a Passivhaus founder having to come up with a budget retrofit strategy for an existing home in my area. With all due respect, I think you'd come back in an hour and find them under the table rocking back and forth muttering to themselves.

  28. wjrobinson | | #28

    Andy, great post.Ken.... no
    Andy, great post.

    Ken.... no soup for you. You might want to relay this thread to your father in law so he might be able to explain how often life... is not... anything to do with... a bell curve. And nagging your wife...? OK... And not getting the gist of this blog? PH is a great concept... but... Martin is telling it like it is Ken.

    No soup for me either... just to be fair... and please don't continue with me... thank you... have a nice day my friend.

  29. AndyKosick | | #29

    Duct sealing and air flow.
    Read some posts and had to comment on this because because I've had a problem with this. I sealed some ducts in a crawl and the furnace started high limiting regularly. System airflow is important to the furnace operating properly and should be tested. Tell me if I'm wrong but this should be as simple as an acceptable range of total external static pressure for a given model of furnace (note that I'm not talking about system balancing for comfort just the furnace operating properly).

  30. user-943732 | | #30

    comments, clarifications and replies
    Sorry I'm late to the party here and this comment is so lengthy, but there was a lot to cover.

    Martin- I think you did a very good job of trying to summarize the wide range of items I covered in the talk, but I'd still like to clarify a few things and reply to some questions:

    1) I think current modeling tools work pretty well in modern and fairly efficient homes and can work pretty well in older homes -- if you make some tweaks to a few things like R values and infiltration and duct models. I wouldn't want anyone to think that I'm claiming that my simple model has some type of magic fairy dust driving it -- it's just a matter of adjusting some default values and assumptions and tweaking a few algorithms and focusing on getting the big things right.

    2) I'm not against energy modeling -- I'm only against overly elaborate modeling efforts that involve spending a lot of time trying to model things that either don't matter or can't be modeled well. For energy retrofits, modeling tools should be quick and easy to use and only ask the user for input about things that matter. The model should be a tool to help the auditor -- not the other way around ;} For new construction or large or unusual projects, more complex modeling may be more justifiable.

    3) Manual J is a fairly crude model and simpler methods can work as well. The relatively limited number of sizes for available heating and cooling equipment, the very small energy penalties from oversizing modern HVAC equipment, the desire for pick up capacity, and the advent of modulating equipment are all making the specific equipment size less important than ever. The biggest benefit of "right" sizing may be the increased likelihood that the ducts will be big enough. Manual D is more important than Manual J. None of this should be interpreted to mean that I think oversizng by 400% is OK.

    4) Duct testing -- my comments were about duct blaster testing. It is the time and hassle of taping the registers and attaching the duct blaster that make it not a cost-effective test in my opinion -- especially given the effectiveness of pressure pans in finding leaks if you have a blower door with you to address shell leakage. Air flow reduction from duct sealing can be a problem, but you can track the impacts by measuring static pressures.

    5) I've seen many people claim the supernatural performance of PHPP but the data I've seen (and common sense) don't support many of these claims. Sure, houses with R-40 walls and R-60 attics and U-0.1 windows will use very litte heating energy and so absolute usage errors will tend to be small. But even for heating use -- how much a given super insulated home uses may be more about the type of TV they buy or whether they have a large dog than whether the slab has 4 inches or 6 inches of foam under it. In addition, if thermal flaws at window details really matter it still doesn't make sense to me that every home would need a custom analysis -- can't you learn from prior homes? I really can't even follow the logic for most of Ken Levenson's arguments and agree with Martin on nearly every point. 90% savings? certainly not compared to most new homes.

    6) I also find the arguments about why ROI doesn't matter to be quite weak. The ROI for my vacation is more than 100% or I wouldn't have done it -- the enjoyment was worth the cost. What enjoyment do you get from thicker walls or lower U value windows. Comfort? you can get plenty comfortable without going to PH levels. Energy features with poor financial ROI likely have poor climate change ROI too -- especially when you consider opportunity cost. I'd rather see the resources that go into imported super windows and super HRVs and extra foam under slabs go into retrofitting some of the thousands (millions?) of uninsulated homes that many low and moderate income households live in across the US. If you really want to have a big impact on climate change you could build a house to BSC level (5/10/20/40/60) and then take the extra money saved and donate it to retrofit a few low income homes in the area.

  31. GBA Editor
    Martin Holladay | | #31

    Response to Michael Blasnik
    Thanks for your detailed comments -- very helpful.

    And it's perfectly OK to climb on a soap box now and then. I strongly agree with your point that "Energy features with poor financial return on investment likely have poor climate change return on investment too -- especially when you consider opportunity cost."

  32. kevin_in_denver | | #32

    IMO: Energy Models Aren't Intended to Predict Usage for a House
    Occupants are utterly unpredictable. Build quality is fairly unpredictable. So prediction has always been a loser's game. I think that's the main point here.

    What, then, are models good for?

    They can be useful for comparing a variable, one at a time.

    Example: How much (if any) will I save per year using high SHGC low e windows vs. standard low e windows ?
    OR, How much will I save per year with R28 wall insulation vs. R19?

    But even then your answer is tempered by: "assuming this set of standard conditions".

    So you build a few models, make a few changes, note the results, then stick your modeler back in the drawer. Drag it back out when the technology or costs change significantly.

  33. GBA Editor
    Martin Holladay | | #33

    Response to Kevin Dickson
    I understand your point, and it's valid, with just one quibble: Blasnik has found entirely different reasons to explain why some energy models give the wrong answers.

    It's not because occupants are unpredictable. And it's not because build quality is unpredictable.

    Blasnik has looked at the performance of energy models for thousands of houses, and the models are wrong in systematic ways. That's why he knows that these problems aren't due to the unpredictable behavior of occupants or variations in build quality.

    The reasons for the systematic errors discovered by Blasnik are given in the article; they include bad default assumptions and bad algorithms. These are problems with the models themselves, not problems with the occupants or builders.

    One of the problems cited by Blasnik -- a bad default R-value for single-pane windows -- can potentially throw off an energy model used for one of the purposes you suggest (determining savings attributable to glazing specifications).

  34. JPGunshinan | | #34

    Value of Energy Modeling
    I think a good diagnostician is worth her/his weight in gold. Others may benefit from a lot of testing and modeling.

  35. watercop | | #35

    Diaggregating vs modeling
    I do light to moderate energy retrofits in north Florida. Generally the sole fuel is electricity but occasionally I run across some wayward soul with a center flue gas fired storage water heater fueled with $4 per gallon propane (gas is cheaper, right!?!?)

    I don't use any energy modeling software, but i push hard to obtain at least 12 months past energy bills. Then I work on disaggregating them.

    I organize my analysis around a home's six or more Energy Centers, to include:

    1) Heating and cooling - identify an approximate proportion of total energy use attributable to HVAC, typically 30-40%. Assess present system, run a Manual J, room by room. Measure room supply airflows, compare to client complaints about hot and cold rooms - every home seems to have at least one problem room. Discuss system sizing and humidity control.

    If present system is aged and inefficient, calculate and advise if a cost-feasible combination of building enevelope upgrades (infiltration sealing and insulation) would allow for a smaller, high efficiency variable capacity replacement system. Using Man J and equivalent full load hours, conservatively calculate likely annual savings from system upgrade alternatives.

    2) Water heating - default here is a storage electric water heater, generally in the garage. For homes with 3 or more full time residents, a heat pump water heater is generally a no-brainer. Homes housing 4-5 people may additionally or alternatively benefit from a refrigerant desuperheater recovering waste heat from the central air and parking it in a preheat tank plumbed upstream of any conventional water heater, including heat pump units.

    If a client is in doubt or otherwise desires to refine water heating cost it is a simple (less than 10 minutes / $50) matter to temporarily connect hourmeters to a storage electric water heater's elements...wait a week and crunch the results, adjusting as necessary for present vs year round average cold water inlet temperatures.

    Homes with 6+ full time residents MAY benefit from a solar thermal domestic water heating system, but their high first cost, complicated installation hobble their effectiveness and ROI when all is said and done...the recent article "solar thermal is dead" loudly resonates with my experience and data.

    3) Laundry - ask number of loads per week, note type of washer (front or top load), water temperatures selected. Determine if dryer has a moisture sensor, whether client selects automatic drying cycle. Also assess dryer exhaust path for airflow and lint blockage.

    4) Media - question client as to number and types of computers and TVs, daily hours of operation. Connect a Kill-A-Watt to main media power strip to quantify load if circumstances indicate.

    5) Kitchen - assess average cooking intensity, fuel and frequency. Examine range venting system for IAQ and humidity removal efficacy.

    6) Lighting - Assess percentage of fixtures utilizing high efficiency lighting (CFL or LED) Inform that incandescent fixtures are both inefficient source of light and add significant cooling load. Ask client to list lamps and average daily hours of use.

    7) If a pool is present determine filter and other pump horsepower, daily hours of operation. measure motor amperage, calculate power consumption.

    8) Assess other significant loads - out buildings, water features, hot tubs, irrigation pumps, etc.

    Armed with all that I can confidently suggest energy improvements and predict with reasonable accuracy their effect on energy conservation and operating cost.

    If circumstances dictate, I temporarily deploy a multi-channel Energy Detective to gather and present consumption data for multiple significant loads in the home.

    My point is that accurately disaggregating actual client utility costs trumps modeling software.

  36. QgHi6BXwJK | | #36

    duct sealing and lower air flow
    In general duct sealing only does not seem to decrease air handler airflow. Fixing disconnects, ducting building cavities, and fixing holes bigger than your head sure can,. Measuring pre and post static pressures is good idea, although I find a flow plate test faster and less variable. If I didnt own a flow plate, I would do pre and post statics


  37. user-1005777 | | #37

    Homeowner perspective
    I own a Mini home (Trailer) 16 ft by 70 ft, built in 1992. My concern is total energy cost per year. I had a blower test done in 2007 and the ACH at 50 Pascals was 6.9. Without any detailed modeling we started air sealing. Shotgunning the entire house we reduced that to 3.1ACH. We installed a mini split heat pump at a cost of $4200 instead of the recommended window replacement that would have cost $5000.ROI was done on a calculator and the savings were better with the heat pump. The house is all electric, and my base load per month is 500Kwhrs.If I take the baseload off my bill, I will have used about 3000 Kwhrs to heat my house this winter, at a cost of $295.50.
    Going to attack the DHW next. We use an average 7.3 KWhrs a day on hot water, but 1.7Kwhrs of that is heat loss from the tank. When the tank needs replacing I will get a tank that loses 1.34 KW a day and enlose it with a preheat tank. The preheat tank will never get up to the house ambiant temperature so it will recover the loss from the electric water heater. That will reduce our DHW to 5.6 KWhrs a day $58 dollars a year saved for an incremental cost of $500. If the enclosure is relatively air tight it should reduce the sweating on the preheat tank.

    I realise that the 1.7 KWhrs will have to be replaced by the heat pump at .567 KWhrs a day, but I also will gain that in heat flow to the DHW from the house in the summer.

    Any suggestions?

  38. Alan Abrams | | #38

    not ready to bail out of the modeling business yet
    completely fascinating article. in the case of retrofits, it seems that the greatest errors derive from over-estimating pre-retrofit energy use. However, it is easy to compare the estimate with actual use.

    In general, I'm reluctant to accept the conclusion that energy modeling is a waste of energy. just because present systems may be inaccurate is not sufficient reason to neglect the concept--it should motivate us to improve our chops.

    If that is a valid pursuit, then it addresses another of the author's issues--the variance of owner behavior. If we can develop and adopt a good modeling system, then it puts owner behavior into a rational context. A reliable system would quickly inform Harry and Harriet Homeowner what the impact of their four tv sets actually has on their utility bill.

  39. user-1118486 | | #39

    Energy Efficient Homes v.s. Occupant Behavior
    Thanks, Martin. (hi, Michael)
    Great topic and good write up of it.

    Having delved into this topic deeply and as the author of the Energy Trust of Oregon / Earth Advantage study you linked to in the article, I can say I was shocked by how poorly modeling software works. That said, I do think there is a time and a place for modeling. All things being equal, as in comparing different designs of the same (new) home, modeling can help one find the relative value of thicker insulation, versus better windows, versus a tighter envelop, etc. I still use PHPP, of all things!!

    I wanted to point out one thing that seemed to get confused at times in the article and comment thread. If you are measuring the efficiency of a building, you quickly get lost in the woods if you add in occupant behavior. If you want to model occupant behavior (as I think Michael would agree) the best way is to look at their previous utility bills, not model the house. Yes, people will use more or less energy in a house and the number look very different. But keep clear about what you are measuring and why. If you want to compare the efficiency of one house to another, you better normalize for occupancy behavior. If you want to help people use less energy, talk with the people, teach them some new habits (good luck), and let them know about some more efficient options for the things they plug in. Pages 50-53 and 60 in the study mentioned go into this in more detail.

    Starting on page 53, I talked about what we thought was reasonable and possible for the accuracy of a modeling software program. Michael’s SIMPLE was close, and we thought it could be developed to get there. It also strongly made the point you mention in the article, that more input doesn’t make it more accurate, so you might as well keep it simple and save everyone a lot of time and effort.


  40. GBA Editor
    Martin Holladay | | #40

    Response to Bruce Manclark
    Thanks for your comment on duct sealing and airflow -- much appreciated. And thanks for all your good work in the Pacific Northwest.

  41. GBA Editor
    Martin Holladay | | #41

    Response to Roger Williams
    It sounds like you've been making good energy retrofit choices, using a step-by-step, logical approach.

  42. GBA Editor
    Martin Holladay | | #42

    Response to Alan Abrams
    I agree that energy modeling can be very useful, as long as we choose good modeling tools. Michael Blasnik has shown the potential of improved tools by developing his Simple spreadsheet.

    One of the goals you mentioned -- "a reliable system [that] would quickly inform Harry and Harriet Homeowner what the impact of their four TV sets actually has on their utility bill" -- does not require energy modeling software. All it requires is a real-time electricity use meter with a living room display. These devices exist; for more information, see Home Dashboards Help to Reduce Energy Use.

  43. GBA Editor
    Martin Holladay | | #43

    Response to Eric Storm
    Thanks for your comments, and for your excellent report.

    I completely agree with your important point: "If you are measuring the efficiency of a building, you quickly get lost in the woods if you add in occupant behavior. If you want to model occupant behavior (as I think Michael would agree), the best way is to look at their previous utility bills, not model the house."

    That said, it's important to repeat that the modeling defects identified by Blasnik have nothing to do with occupant behavior.

  44. user-1092370 | | #44

    Fascinating Article - Lets not throw the baby out with bathwater
    Really interesting article. I'm actually a (relatively new) proponent of energy modeling - especially for gut reno and new buildings. It seems to me that the main issues with energy models (bad assumptions, bad algorithms) are fairly simply solved.
    Make better assumptions. Use better models.

    We aren't technologically frozen - energy models will improve (especially if we can be open source).

    Look, nobody is arguing that there aren't regional builders out there with deep intuitive knowledge developed over years of experience. The simple fact is - not every designer has this engrained knowledge and respect for the vernacular (pop into a design school critique - vernacular design would be considered one step above the kid who draws like a two year old (sadly)) and not every designer practices in a single climate zone.

    There is a recurring question of 'Is energy modeling cost effective?' In a vacuum, I don't know. But, as an architect, I don't see it as a singular service. I think of it as a design tool. Like sketching, or 3D modeling, energy modeling allows iterative idea proposal, testing and refinement. It is integral to the design process. Its not for every job (neither is 3D rendering), but its valuable for new buildings and gut renovation.

    Arguably, REVIT (and CADD in the 80s) isn't exactly cost effective for all firms right now. It may not even be the most ideal tool for all projects. But architects and engineers will need to use it because its a valuable design and drafting tool that will only improve. In ten years, if you aren't using REVIT, you're going to be at a competitive disadvantage. I think the same applies for energy modeling.

    As to this backlash against PHPP and Passive House (which has been shown to be an effective modeling tool). I have to be honest - the arguments are just a little, well, curmudgeonly. The critiques are tangential - 'so and so website made an incorrect claim (in this day and age! the horror)', and 'wouldn't we better off addressing climate change on a meta level'. Both of these may be true, but it really doesn't address what is the fundamental value of Passive House - it is a methodology aimed at bringing a PARTICULAR SECTOR (housing) into line in regards to Carbon emissions. Do we need to address energy on a holistic scale? Do we need to promote retrofit, especially in urban cores? Yes, yes and yes! But none of this is mutually exclusive to Passive House.

    But, do you know what I think the real issue is? I think that builders and designers who have been doing GREAT work for years and years, don't like being told that, if you don't meet Passive House, you're not doing good enough. I don't have a response for that, but I suspect that, psychologically, this is an underlying issue.

    Maybe we should hug it out.

    Sorry for the long, rambling and slightly pugnacious post. I really love this site and love the discourse! I think we're all rowing in the same direction, we just have different types of oars!

  45. GBA Editor
    Martin Holladay | | #45

    Response to Grayson Jordan
    I agree with most of your points. Your admonition -- "Let's not throw the baby out with bathwater" -- was similar to mine ("Don't throw your energy models out the window").

    And I don't doubt that PHPP is an accurate modeling tool -- I just wonder whether the hours spent entering data are hours well spent.

  46. XUXfjRJt2K | | #46

    Ready, Fire, Aim
    There is an old expression: "Models model modelers," and it couldn't be truer in the case of building energy models. On the one hand, these models mirror their authors'; view of the world, plus, often more importantly, their users' skill in assigning inputs and interpreting the outputs.The results of a four-year-old Energy Trust of Oregon analysis referenced in Martin Holladay's recent post are more a product of how the models were used than what the models were capable of.

    The Oregon study has been repeatedly invoked to make a series of points. But, citing a flawed study over and over doesn't make it true. In fact, such repetition does a distinct disservice to the building energy community--creating a mythology of misinformation.

    Truly useful accuracy assessments couple a rigorous and transparent methodology with constructive forensics to help understand the sources of inaccuracies and provide fodder for improving the models. While deficient in these respects, the Oregon study also did not "accurately" characterize the building energy models. The authors chose not to heed review comments from developers of the REM/Rate tool pointing out specific deficiencies in the methodology and analysis. The final product was insufficiently documented to allow independent validation of the results. After publication, the Energy Trust of Oregon opted not to respond to requests for more transparent documentation to help identify the sources of asserted inaccuracies. What we can tell from the document is that the study hamstrung at least one of the tools--the Home Energy Saver (HES)--in multiple ways. Other problems with the experimental design are too numerous to go through here. We have now rerun HES against these same Oregon homes with greater quality control of the input data and full inclusion of known operational and behavioral factors. The median result agreed within 1% of the measured energy use, and with much lower variance between actual and predicted use than suggested in the Oregon study. For those who prefer not to consider occupant behavior ("asset analysis"), HES still predicts the central tendency of this set of homes much better than represented in the Oregon Study (but there is more spread in the results and many more outliers).

    Unfortunately, the Oregon study (and more recent derivatives) has become the thing of urban legend: 'No reason to bother with detailed simulation. It is not accurate, nor worth the trouble.' However, in writing this response we wish to very strongly contest this conclusion and to let readers know that our recent research indicates the opposite is true. Not only do detailed simulations work well, they work better than simple calculations and provide greater predictive ability, especially when the more detailed operational level characteristics are considered.

    Oddly, even taking the study at face value, perhaps its most important charts (not among those included in the GBA article), and other metrics found in the report show HES performing better than the other tools, including as defined by symmetry in the distribution of errors around the line of perfect agreement. This fact was de-emphasized in the study, and instead the readers' attention was directed primarily to average (rather than more appropriate median) results, coupled with a focus on "absolute" errors (obscuring problems of asymmetrical errors in some of the tools and inability to track realistic envelopes of energy use). All of this points readers toward a misinterpretation of the relative accuracy of the tools.

    The refrain about simpler models producing better results is a red herring. Indeed, as show in the Oregon study, the more highly defaulted version of HES (dubbed "HES-Mid") had far less predictive power than the "HES-Full" version. Moreover, while HES offers many possible inputs (ways to tune the model inputs to actual conditions), many of them were skipped in the "HES Full" case, in lieu of set default values that were not representative of the individual subject homes. Additionally unclear to the reader, the Home Energy Saver does not in fact require any inputs other than ZIP code, thus leaving tradeoffs regarding time spent describing the house up to the professional using the model rather than to some remote third party. Of course, some inputs are more influential than others and analysts should focus on the ones that matter most for the job at hand.

    That said, the amount of time that running a model "should" take (and the role of operational versus asset attributes) is a function of the purpose to which the results will be put and the definition and level of accuracy required.There is certainly no one-size-fits-all solution.This fact is rather glossed over in the article. User interface design also has much to do with the ease of model use and hence the cost. Moreover, thanks to long-term support from the U.S. Department of Energy, the Home Energy Saver is available at no cost to all users, which helps reduce the ultimate cost of delivering energy analysis services in the marketplace.

    On the other hand, it is wishful thinking to suggest that simplified assumptions can capture the complex reality of estimating home energy and the potential for savings, and doing so results in a hazardous folk tale. Indeed, in a new peer-reviewed study of HES accuracy to be presented at the 2012 ACEEE Summer Study, we show that simulation is a very powerful means to improve predictions of how buildings use energy. Our analysis, however, heralds the uneasy conclusion that the importance of the household occupants and their habits is on a par with that of the building components and equipment.

    Whether or not open source, it is important that these models not be "black boxes" and that the user community is free to discover what is happening under the hood. Extensive documentation of Home Energy Saver is open to public and peer review and suggestions on improving the methodology are always welcome. Public documentation of the other tools examined in the Oregon study is much more patchy.

    All other things aside, many building energy models are in a state of constant improvement. Dredging up a dated analysis that was flawed in 2008 produces an even more flawed impression four years later as these tools have evolved. Indeed, this may have been the most useful result of that the Oregon study: to draw critical attention to model predictions in a number of areas: infiltration, hot water estimation, HVAC representation and interactions, thermostat uniformity etc. Suffice it to say, that observations from the microscope are now reflected in more powerful and robust simulations four years later. And that was a positive impact. Moreover, no one in the simulation community is standing still; further improvements are being made as our attention is drawn to further phenomenon. Did you suspect that interior walls might influence heat transfer substantially in poorly insulated homes? Or that basements are seldom ever conditioned to the same levels as the upstairs? We did. And addressing these shortfalls are bringing the space heating predictions of HES into ever-closer correspondence with actual consumption.

    Beyond Oregon, we are looking at high-quality data sets that span the gamut of geographical variation and housing types: from cold Wisconsin to hot and humid Florida, often looking at fine-grain measurements of end-use loads from monitoring studies that allow further insight.

    We fully agree that many nuances in building science may not be well reflected in a given model, and that bad inputs will yield bad outputs (garbage-in; garbage-out). These are areas of intense ongoing research and improvement in the Home Energy Saver tool at least. All would also no doubt agree that models are no panacea.The map is not the territory, but it can still help you get to where you want to go.

    Simple models can provide rudimentary insight and that should not be under valued. However, the most detailed tools of today can help one to fathom the deeper influences that determine energy use in our homes. Yet, they teach this with a price for admission, suggesting that understanding comes not from simplicity, but rather from its opposite. If you are out to describe the truth, Einstein famously said, leave elegance to the tailor.

    Evan Mills, Lawrence Berkeley National Laboratory
    Danny Parker, Florida Solar Energy Center

    Discussion continued here.

  47. user-943732 | | #47

    reply to Evan and Danny
    Reading your post made me wonder if we've both been working in the energy efficiency field for as long as I know we all have. The Oregon study was not the first or only study to find major problems with energy use predictions from standard (complex) building energy models -- Martin even mentions the Wisconsin HERS study as well as results from retrofit savings studies that I brought up in my talk. If you talk with almost any thoughtful person who has run energy models on many older homes in the real world and then looked at the energy bills, you would have been hearing the same thing over and over for years -- you can't trust the model's energy use estimate or savings estimates. Heck, I've even run my own (100 year old) home through REM and HES and both over-estimated my heating use by more than 40%.

    You can't really blame these huge errors on occupant effects or auditor data entry errors when you find large and systematic biases -- houses with low insulation levels and high air leakage rates use much less energy than "standard" model predict. There are reasonable explanations for why these errors are there and fairly easy ways to reduce the errors.

    The model estimates of heat loss through uninsulated walls and attics and from air leakage (to name a few) were very poor. If you estimate retrofit savings from reducing air leakage by 1000 CFM50 or insulating uninsulated walls using these tools (or at least the versions from a couple of years back), you get estimates that are far larger than all of the empirical data from impact evaluation studies.

    The Oregon study was expected to help quantify how much accuracy you lose when using a simplified model. The finding of apparently better accuracy was a surprise to many. But the SIMPLE model was only more accurate on these homes because it didn't make the same huge errors as other models . The fact that these errors existed for so many years is a testament to how either no one seemed to look at this stuff or else perhaps it is due to an unflagging near-religious belief in the accuracy of models which leads model makers to attack and dismiss any empirical data that contradicts their models.

    The lessons from the Oregon study are not that simpler models will always work better than complicated models -- obviously if you have more detailed and accurate inputs for a model it should be able to make better estimates of energy use if it uses good algorithms (still a big if). But the lesson was that the current complicated models -- including REM and HES -- were remarkably inaccurate in modeling older homes and that if you don't get the big things right it doesn't really help to do a good job on the many small details -- in fact they become a waste of time.

    Speaking about getting the big things right, I'm glad to hear that you've used the results of the Oregon study to actually revisit some of the assumptions and methods in Home Energy Saver. But I'm a little confused about how such a deeply flawed study -- one in which you claim the models are actually unbiased when done properly -- could help identify model flaws. Regardless, it's good to get past stage 1 -- denial -- and move through the remaining stages towards acceptance as quickly as possible.

    When used in the real world of fixing homes, energy modeling should be subjected to the same cost-effective criteria as retrofit measures. It is up to the models out there to demonstrate that they provide useful information that is worth the time and effort to collect the data and run the software. I think that -- even when the models are improved and better reflect real world energy use -- people will still find that elaborate modeling may not be worth the effort very often. The best modeling software will only ask for things that are important and measurable, provide answers that are consistent with empirical data, and have a streamlined user interface to make it a useful tool rather than an administrative burden. I think we may all agree on that.

  48. GBA Editor
    Martin Holladay | | #48

    Who's to blame when complicated models give the wrong answer?
    Thanks, Michael, for your detailed response.

    To Evan Mills and Danny Parker: thanks to both of you for your many years of research into the issues we're discussing here. I'm honored to have you comment on this blog, and am delighted that GBA can be a forum for this important debate.

    I'd like to comment on one of your assertions: namely, that in order to obtain more accurate modeling results for the houses used in the Oregon study, you have "rerun HES against these same Oregon homes with greater quality control of the input data and full inclusion of known operational and behavioral factors."

    In other words, if the answer is wrong, then clearly it's necessary to adjust the inputs.

    I hear that the same approach is used with another well-known modeling program, WUFI. John Straube warns untrained users to be careful of WUFI, because any WUFI results need to pass the smell test. (In other words, if WUFI says that a certain type of wall will fail in a certain climate, and remodeling contractors working in that area know that walls of the same type are not failing in the field, then it is necessary to go back and tweak the WUFI inputs until the results pass the smell test.)

    When I hear researchers tell me stories like this, I conclude that the model isn't very useful.

  49. user-1120174 | | #49

    More on the subject...
    There was no "input massaging" that took place in the re-analysis of the Oregon data. The only adjustments, of which I am aware, were that cases with wood heat and supplemental electric space heat were censured-- a reasonable precaution to help examine the accuracy of end-use prediction.

    However, a great many adjustments were made to the simulation algorithms over the last years-- many of these in response to greater scrutiny of comparing model to billing records. If one wishes to assign guilt to that process, then we (and the entire simulation community) are fully at fault. As mentioned, in the blog entry, I credit the Oregon study with starting the ball rolling on that process. I just don't think that you should condemn simulation for limitations that may have been addressed-- and are, indeed, being continually improved.

    To me, I see such changes as signs for optimism and encouragement. We are doing better than ever before and ferreting out the various factors that cause the models not to work so well. Right now, HES is predicting quite accurately for space heating where we have data on space heat end-uses.

    Electricity is more problematic, perhaps because electricity use, itself, is a more stochastic process and less accessible to prediction.

    Again, simulation models are powerful: HES, BEopt or EnergyGauge can clearly show a user that energy efficiency lighting is critical in the south where lighting savings are trumped by additional cooling systems. On the other hand, wall insulation may be a better early retrofit option in Minnesota where part of the lighting savings are lost due to increased heating.

    Of course if users want to plan zero energy homes using rules of thumb and personal divining rods, then be my guest.

    I'll put my faith in physics and detailed modeling.

    Danny Parker
    Florida Solar Energy Center

  50. GBA Editor
    Martin Holladay | | #50

    Response to Danny Parker
    Thanks for your further comments. I'd like to emphasize what I see as our points of agreement:

    ● The Oregon study revealed defects in some of the algorithms in HES and REM/Rate.
    ● Due in part to the Oregon findings, the HES algorithms have been improved.
    ● While energy modeling may not be appropriate for every energy retrofit project, it is useful for designers of custom zero-energy homes.

  51. user-943732 | | #51

    How detailed is detailed enough?

    it may surprise you, but the SIMPLE spreadsheet model does include the interactive effects between lighting and heating and cooling loads. it also includes the heat/cool interactions for hot water, plug loads, etc. -- and also the interactions between cool roofs / radiant barriers and duct system efficiencies and attic heat gains/losses.

    I guess you could say that SIMPLE is only simple when it comes to reducing the number of required inputs and avoiding hourly simulations. But it is actually fairly complex under the hood. I tried to include significant interactive effects throughout. It's not at all clear that you need to use an hourly simulation or collect a lot of extra data elements to capture these effects reasonably well.

  52. fhAeDkdb2L | | #52

    Modeling use cases
    So why do we model currently and do the modeling tools actually support those use cases? Simply tossing rocks at modeling in general is doing a disservice to the industry when there are actually strong use cases for modeling. That is not what Michael is doing, but it is easy for modeling critics to make it seem that way.

    So what are the basic use cases for modeling?

    We model to design efficient new homes where potentially each feature could be changed to improve energy use and there is no historical usage for comparison. Seems like detailed modeling might be useful here and it sounds like the models are actually OK at this. You can get picky about which model to use and how each of the models support calculating really low energy use but that is a matter of personal taste and the style of building to some extent. I understand that there is testing underway to compare REM to the Passive House Model to allow REM to be used in that program.

    We model to help make us support decisions about retrofit options. We have a billing history to help us improve our pre-retrofit model (if we make use of the billing data to calibrate the model and find data entry errors) and we learn a lot about what saves energy and what does not. But after doing a lot of models do I really need to keep modeling? What more am I learning? Not much. But in my experience, those building performance contractors who have not modeled some good number of homes (50-100?) are not as strong at understanding where to cost effectively save energy and tend to over design energy saving solutions for customers, spending more of the customer’s money unnecessarily. So modeling to learn is good. But modeling once we have learned may not be as valuable.

    We model to get incentives. Programs that want to encourage deep retrofits like to use modeling to control access to dollars. That way you get to design the best solution for a home. Deemed savings programs do not fit well with home performance approaches because they tend to have you putting things into homes that do not need them just to get incentives. Other funding sources, such as Congress, like performance based incentives because everyone wins (most everyone anyway). They don’t have to go through getting lobbied by different industries to their energy saving solution included in the law. At a different session at ACI, Jake Oster of Congressman’s Welch's office stated this explicitly. We currently have two national incentive programs introduced with bi partisan support that depend on modeling. Boy wouldn’t that make an impact if these passed! And since they create jobs and have bi partisan support they have a not so terrible chance at passing.

    Is the modeling process for getting incentives robust enough to trust with determining incentive values? The proposed laws reference several requirements that were designed to make the process robust and enforceable through quality assurance. First, they require that the software pass the RESNET Audit Software standard. These are a set of physics tests that stretch the tools. Stretching is a good test for relative model robustness but this type of testing is different than the accuracy tests that measure both user error (often complexity driven) as well the performance of the physics at the mean. These tests can and will be improved but they are the best available solution now for a performance based (meaning developers can run the test themselves which helps you develop faster and less expensively) accuracy test that can be referenced as a standard in legislation.

    Second, we need to establish a baseline for the energy usage in the building. This is what the incentive dollar amount will depend on. But guess what, we have a simple method for getting a baseline. It is the existing energy bills. If we subtract the weather normalized energy use from the building that has gotten a whole house improvement, we ought to be fairly accurate. The real bills are the real bills after all and we have data showing that improved houses model better than house with all sorts of problems. It is the problems that are the problem. They are hard to model correctly since there are lots of inputs needed, many of the inputs are hard to measure so we estimate them and tend to estimate high and all these assumptions about problems combined with any errors in the modeling software for the actual conditions combine to make it tough to model poor performing homes. But for an incentive calculation we can actually ignore these issues. We can make the baseline the weather normalized actual bills.

    But there is an interesting thing. An energy model calibrated to the actual weather normalized bills is functionally the same as the weather normalized bills. And if we calibrate the model we get the extra benefit of eliminating a lot of gross user error that otherwise creeps into the models. So we solve two things at once, we get an accurate baseline and we improve model quality.

    BPI has worked with RESNET and a group of industry contributors to create an ANSI standard for this calibration process. This standard is what is referenced in the legislation. The joint BPI and RESNET effort here was considerable and very important. Congressional staff did not believe we could make this joint effort work but we did and congratulations are due to the participants. The joint effort also means that the bill is more likely to pass. The President has endorsed these efforts in his budget, getting the attention of DOE in the process.

    Other programs besides Congress use performance based incentives. These programs would benefit from setting baselines using model calibration also. Efforts like Green Button and utility connections to EPA Portfolio Manager also improve access to the energy information needed to do the calibrations. Other efforts like HPXML will make it possible to choose modeling tools and to collect data outside of modeling tools and import that data into modeling for incentive access. There is a lot of infrastructure growing that will make performance based incentives more cost effective to perform and administer.

    Finally, we can use modeling to track results. If we don't know how much we expect to save it gets pretty hard to figure out if we are hitting our targets. So modeling helps us improve the performance of our work if we can get access to post performance data and compare it to expected results. (Long discussion about occupancy here but this post is too long already.)

    But do I need to run a model to put insulation in a house and air seal without a performance incentive? No. It is not worth the effort and the results with models with enough detail to be used for performance based incentives will be suspect unless I take more time than it is worth. A simple approach might be quicker.

    There are a range of simple approaches that reduce user input and error and would make contractors lives easier. But our experience is that a strong minority percentage of program participants like the detailed modeling. So there is no one size fits all. If you make it too simple, some people complain and if you provide more detail people complain about that too. Oh well.

  53. user-1120174 | | #53

    The Future

    Glad to hear that SIMPLE accounts for the utilizability of internal gains. However, while I particularly like your laundry list of things that have plagued simulations-- and we have worked to address those in current efforts-- I still remain unconvinced that simulation is not the superior tool.

    I like to see how loads line up with PV output by hour. And I like the possibility of evaluating technologies that I think have future promise, but complex interactions, such as heat pump water heaters being used to scavenge cooling in hot climates, but being sensitive to location. Hourly simulation whether HES, BEopt, TRNSYS or EnergyGauge allow those possibilities and if we address many of the short comings you mention, we are left with a more powerful saw.

    Finally, tools such as BEopt and EnergyGauge with its CostOpt module allow economics to be blended into the evaluation process. This, in turn, allows hundreds of exhaustive simulations to be performed (beyond the patience of reasonable analysts using a single spreadsheet or single set of favorite parameters in a simulation) to locate the approaches that are likely to produce the most cost effective means of reaching energy reduction targets.

    I liken the situation today, to that of chess computers 15 years ago. While one can study chess and become reasonably good, fifteen years ago, even a reasonably strong player could best computer programs. Now, the tables have turned and even a grandmaster cannot challenge desktop software. World Champion, Vladimir Kramnik was crushed by Deep Fritz in 2006. Since then, the gap has widened.

    Admittedly chess is a mathematically deterministic system, but except for the occupant related variations, this is largely true for buildings too. Physics matter. And if the current understanding is flawed (as you have pointed out several short comings such as windows), it can be made right. [A current favorite problem assumption for me of late is the assumption of uniformity of temperature in buildings-- which can't be right because of interior walls]. Well, there is Passivhaus.

    Anyway, improvements are already manifest.Just a matter of time and effort, as we have been involved with recently with HES. We're getting better-- a lot better.

    Eventually, I envision software that will best the energy predictions of human operators-- at least in terms of locating the best performing or most cost effective options.

    And if you want to add occupant related variation-- think Monte Carlo simulation with triangular probability distributions. Get ready. With billing records, judicious tests and a series of probing questions, the "Deep Energy Blue" of tomorrow will know how to get there in the way you want, and in a way that is more clever than any of our preconceptions.

    They will eventually be able to learn about their own modeling shortcomings and suggest code and or algorithms for review and revision.

    By then most of the tedium will be gone; the computer will let you know the level of parsimony or complexity required to resolve the question. However, testing, and good billing data (and even end-use data) will continue to be key requirements.

    Still, don't be surprised if the computer eventually second guesses some inputs, however!

    A long time ago, in a confession telling my age, I eagerly took a college course on the use of the slide rule. What an elegant tool! However, my love affair, was later dashed when Dr. Tamaimi Kusuda (one of my heroes) came up with a method to predict household energy use with a TI-59 calculator, a numbers box, which was God's own salvation for engineers at that point. I was smitten.

    Since then, things have changed a lot. And while he was living, Dr. Kusuda eagerly embraced it all and contributed mightily to TARP that was developed by NBS.

    Having experienced the beauty of the optimizations made by BEopt or CostOpt-- and how much they have surprised me by their clever approach-- I want to be be around to see that happen.

    Yes, you see, I am biased. I find energy simulations beautiful. I just hope they feel the same way about me.

    Danny Parker

  54. XUXfjRJt2K | | #54

    Replies to questions in Martin's Comment #51

    Actually, it doesn't seem that we agree on any of those points.... Here are your three bullet points, followed by our responses:

    "● The Oregon study revealed defects in some of the algorithms in HES and REM/Rate."

    Not at all. First, the opaqueness (thus non-reproducibility) and flaws in the methodology did not give us confidence in the results, and there was no basis for assigning differences between predicted in actual energy use estimates between how the tool was used and hamstrung as opposed to how the tool actually works. Second, the study's results were not presented in a way that would have been useful in diagnosing possible ways to improve the model other than perhaps the most vague indications (e.g., look at electricity outcomes versus gas outcomes). Other, higher-fidelity data sets have proven much more useful in this respect. We can't speak for what the authors of REM/Rate may have gotten out of the analysis, but it's a good question and we encourage you to ask them.

    "● Due in part to the Oregon findings, the HES algorithms have been improved."

    No. Given the lack of documentation and no response to our follow-up questions, we could not glean anything particularly useful other than curiosity about the true accuracy of our system. Although we made improvements in the intervening 4 years (!) since the Oregon analysis was done, the main difference between our results and theirs is likely that we removed the handicaps they imposed. The frequency with which the study was cited and misrepresented inspired us to dig more deeply into the question of accuracy. Note that if you read the fine print in the study (and not just the headlines), HES did better than the other tools in various respects.

    "● While energy modeling may not be appropriate for every energy retrofit project, it is useful for designers of custom zero-energy homes."

    No, that isn't our view. It has much broader application than ZNET homes. Again, the choice of analytical approach really depends the underlying purpose of the analysis.

    Danny Parker provides more discussion in Comment 54, above.

  55. GBA Editor
    Martin Holladay | | #55

    Response to Evan Mills
    My attempt to identify possible points of agreement was extended as an olive branch to Danny Parker. I never implied that you agreed with my three bullet points. However, your response is clear; perhaps never before has an extended olive branch been so forcefully rebuffed. Your disagreements are duly noted.

    My comments were directed to Danny Parker, who wrote, "A great many adjustments were made to the simulation algorithms over the last years -- many of these in response to greater scrutiny of comparing model to billing records. If one wishes to assign guilt to that process, then we (and the entire simulation community) are fully at fault. As mentioned, in the blog entry, I credit the Oregon study with starting the ball rolling on that process."

    I don't think I was doing much violence to Danny's meaning by interpreting these sentences the way I did. But I'll let Danny speak for himself; perhaps his rebuff will be as forceful as yours.

    Your third and final point -- that energy modeling "has much broader application than ZNET homes" -- is one I fully agree with. Your attempt at disagreement is based on a deliberate misreading of what I wrote: "While energy modeling may not be appropriate for every energy retrofit project, it is useful for designers of custom zero-energy homes." Syntax and logic compel you to admit that my contention that energy modeling is useful for designers of custom zero-energy homes does not exclude the possibility that there are many other useful applications of energy modeling. If you return to my original blog, you will in fact see an enumeration of four such uses for energy modeling in the first paragraph I wrote. Moreover, the final two paragraphs of my article include a defense of the value of energy modeling software.

  56. GBA Editor
    Martin Holladay | | #56

    Response to Danny Parker
    I'm old enough to have used a slide rule in high school; my father showed me how to use a slide rule when I was still in elementary school. I bought my first calculator when I was in college; it was amazingly cheap -- only $99. (The reason that it was so cheap was that it had a stylus on the end of electrical cord which was used to tap copper rectangles; there were no buttons. That saved the manufacturer a few bucks.)

    I'm afraid that your chess-program analogy is of limited value when discussing building energy software. Computer chess is usually played on a chessboard equipped with sensors, so the computer has access to real-time data -- all data relevant to the game in question. Alas, no one has yet invented a house with enough sensors to provide the relevant home-performance data to a computer.

    That means that for the foreseeable future, we're stuck with ordinary data entry by ordinary humans with limited knowledge of all of the factors governing the performance of a house. Even if we granted an auditor a month for data entry, many of the relevant data points can't even be measured.

  57. user-943732 | | #57

    response to Greg
    Greg- I very much agree with the importance of calibrating models to actual energy use and did some work on the BPI-2400 standard to help push that along. I also agree about the "use case" discussion but I may draw the line differently. My point is that modeling needs to be cost-effective. We need a range of options when modeling a home so that in a typical home with typical energy use and typical problems very little time is expended on collecting data for models or running models. In more complicated situations more data collection and modeling effort may be justified. The challenge is creating the tools that do this seamlessly. I know you are quite aware of the challenges involved in making this a reality and I think we agree that the industry is generally heading in the right direction on this.

  58. user-943732 | | #58

    response to Danny
    Danny -

    I completely agree that, if done well, a more detailed simulation model should certainly be able to make more accurate assessments of energy use and retrofit savings than a simpler model. I have never disputed that. But the key part of that sentence is the "if done well". A simplified model with decent inputs/defaults/assumptions can easily perform better than a detailed simulation with poor inputs/defaults/assumptions. I think that was the main finding of the Oregon study. It also also unclear how much detail and accuracy is needed in each home -- just because we can model something doesn't mean we should.

    But there is also another point worth making -- which is also related to your chess computer analogy -- and that is fundamental uncertainty and the propagation of error.. Unlike with chess computers, energy models of homes we will never be able to provide good field values for many of the modeling inputs -- the distribution of leaks, the site wind speeds, the interior and exterior shading, the properties of the soil, the framing factor of the walls, the regain from duct losses, even the outdoor temperature. The list goes on and on.

    The result of all this uncertainty is that each component of the building model has some minimum uncertainty -- often in the range of 10% and often more. If we assume that these errors are uncorrelated in a given home, then propagation of error tells us that we should sum the squares of the errors and then take the square root. I've done some calculations using what I think are reasonable estimates of these uncertainties and found an overall uncertainty in heating use of 10%-15% (with no occupancy effects). I then re-did the analysis using larger component uncertainties to represent a simplified modeling approach and found that the overall uncertainty only went up by about 5 percentage points even though I increased some component uncertainties by much more. This type of exercise shows that even if you reduce the uncertainty in one part of the building model by a lot, you haven't improved the overall uncertainty very much. It's an open question how much more time and effort it is worth to reduce uncertainty from +/-20% to +/-15% -- is it worth an extra hour at every home? two hours? 10 minutes? Does it even affect our actions -- the retrofits or design? Currently, I think many program designs end up requiring too much time spent collecting data for the building model and running the software. Better software design could help change this conclusion and simplified models may be a part of getting there.


    p.s i would not suggest that people use spreadsheets out in the field to model homes -- that is why I have worked with groups like Earth Advantage, CSG, and energysavvy so that they can turn algorithms into actual software. Optimization methods can be applied to any set of algorithms whether they involve hourly simulations or not.

    p.p.s. To further show that I don't think complicated simulations are useless, I am currently using a dynamic simulation model with a 30 second time step that I developed to explore the dynamics of HVAC equipment control strategies.

  59. user-1120174 | | #59

    Luddite lite?
    Dear Friends,

    Not sure how much longer I can keep this up. Not sure I am really helping as I don’t seem to be convincing anyone of my greater faith in simulation. Nor, am I particularly helping myself; my skin is not thick enough. But I will try one last outing as my opinion isn’t ultimately more important than that of anyone else. So I’ll spill my guts again.

    I believe almost everyone in the business is making their best effort to do better to predict and assess energy use. We just don’t all agree on how to do that. But at least the intentions are good, and I acknowledge that. Reducing energy in our homes is important– very important.

    For me, I am convinced that simulation is the way to go. For instance, something like HES can be run with a bare minimum of inputs– as few as any other strategy one might consider.

    And yet, at the same time, under the hood, HES is running DOE-2.1E which has been hot rodded in recent years. And the problems that Michael Blasnik usefully pointed out are slowly being addressed in a fairly complete fashion. That means that the basic engine underneath the hood is powerful, robust, filled with engineering acumen that is only exceeded by Energy Plus or TRNSYS. That doesn’t mean that a simple calculation cannot be good, or very instructive. It just means that I don’t think it is as intrinsically reliable as an hour-by-hour simulation driven by TMY weather with all the goodies.

    Why not have the most powerful tool running underneath the hood? Even if you have a list of only ten inputs? Do those really well.

    While Evan is correct that the Oregon study did not give the full information necessary to make things better, Martin is right that the controversy generated from that study did result in deep review of simulation methods from top to bottom, not only at LBNL and FSEC, but at NREL as well. That is still going on with the labs checking on each other in the truest deference to the scientific method.

    That multi-year process has borne fruit and continues to result in some surprising findings– for instance Jon Winkler’s dramatic findings on HVAC modeling of the most efficient heat pump equipment and how sensitive it is to the equipment size (through an unintended ARI testing loophole). And there is the more mundane, but much more common influences of insect screening and drapes and blinds on window thermal and solar conductance (much of this being done at the University of Waterloo in Ontario). That, along with the non-uniformity of interior temperature conditions look to be really quite important in simulation.

    But as I said earlier, HES is slowly incorporating some of these things and all of it just makes the results more accurate, more robust and believable.

    Unfortunately, what we have learned in recent months is that billing data is not enough to help troubleshoot our simulations. That happens because compensating errors can shield the analyst from knowing where there is shortfalls or where they do well. We need end-use data so we can see where the predictions fall apart.

    Luckily, we have some detailed monitored data sets where we have such data. And in an ACEEE paper that will be presented this summer, we show how going from “asset” data (and even blind asset data without many details) can be improved by detailed information and finally by “operational level” data. For instance we found one household uses 100 kWh a year for the clothes dryer while another uses 3,500 kWh although they are precisely the same unit. No clothes line involved either. Anyway, you get the picture. Could clothes washer/dryer technology be more important than CFLs, new refrigerator or added ceiling insulation for such a household? Ah, well, yes...for that household.

    And the questions HES can pose to users can help to ferret out such an influence and find that such a situation might exist. And use that to inform users that clothes drying energy might be a critical household energy use.

    Those unhappy with my chess analogy (Gary Kasparov was destroyed by a computer playing a deterministic game that doesn’t include the uncertainty we face with simulation of houses), I have bad news for you. In 2008, a University Alberta computer program, Polaris, won in Las Vegas playing poker against human experts in over 500 hands at a rate of 3:2. Bad enough, that the houses in Vegas are trying to weed out the “pokerbots.” You see, even if the computer was unable to read their human bluffers (an advantage that the human players took advantage of), they compensated by the shear force of neural networks and Bayesian game theory.

    Sorry, but not only are computers good at dealing with deterministic prediction, they also excel in coping with uncertainty. And computers can come to understand where their uncertainty is most critical. Michael is certainly right that we will always face uncertainty in model, engineering and weather. However, we may not agree on the need to use computers to explore that parameter space further.

    For instance, I envision that the computer of the future would feed off past success or failure and learn heuristically what questions and unknowns are most important to get answered or clarified (the partial derivatives to any particular input provides a first approximation). And the answers to certain questions may branch off in the most fruitful directions based on past experience. We are working on such a system for HES now. But rather than a simplified calculation, it uses a very detailed calculation with a truncated series of inputs that are based on an expert system of which parameters are likely to be most critical. Simplified inputs/ detailed calculation.

    What we need more if good quality end-use data to feed the computer to learn about its shortcomings. We’re getting there.

    Yes, simplified calculation can be good. Maybe even very good. But it can’t be best and most rigorous from an engineering standpoint.

    Those who disagree might consider if they would be willing to step onto a “Dreamliner” designed by “good enough” engineering. I’ll anticipate the critics saying that “good enough” for houses is not catastrophic like it might be for a jet liner that exceeds design boundaries.

    But ponder this: might that kind of thinking has to do with retrofit savings estimates that fall short? Or maybe Zero Energy Homes that don’t reach the mark? I wonder how many of the contestants in the Solar Decathalon are using spreadsheets rather than EnergyPlus or TRNSYS?

    I’ll choose improved simulations, thank you. Good luck to those hardy souls on the other side.

    Danny Parker

  60. albertrooks | | #60

    Beauty is where you find it: (Most everywhere)

    Thanks for your comments and your good work:

    "I find energy simulations beautiful. I just hope they feel the same way about me."

    Well... for some reason, I'm sure that they do. I find it a far deeper world than we know. (I hope!)

    This has been an immensely interesting, rewarding and frustrating discussion to watch. I feel like it's the 1900's and I'm witnessing the horse vs. automobile debate: "In 1903, the president of the Michigan Savings Bank advised Henry Ford’s lawyer not to invest in Ford Motor Company, saying, “The horse is here to stay but the automobile is only a novelty, a fad.”

    The need here in a national venue is to continually test the accuracy and value of what is purported to be the "best practice". I appreciate that. It's good for "us".

    However ... We need to keep an open mind, not get stuck in our failures, and push ahead. It appears to me that development work that sets the bar higher creates a vacuum that eventually gets filled.

    Where would the "Pretty Good House" be without the Passive House? By definition "pretty good" can only be quantified by something "better". It was obviously an outcome of Passive House Development in the US. (Note that I hold both of these "schools" in great respect).

    It's wise and great that these modeling programs are being tested and held accountable for their claims today. It's the one thing that will drive improvement for better results.

    I'm neither a good horse doctor or energy modeler... However we both know that it would be foolish to discount energy modeling's development and effectiveness in the next decade(s).

    Cheers to you Danny!

    Albert Rooks
    The Small Planet Workshop
    USA Reseller of the Passive House Planning Package
    (and therefore a BIG proponent of energy modeling)

  61. albertrooks | | #61

    Nice job Ken Levenson
    It's hard to maintain the "value" of more information and choices (PHPP) when it requires more time and energy. My second job at 16 was in a cabinet shop where the owners quality statement was: "Perfect is good enough" It was pretty hard to argue with him.

    We put out some beautiful work...

    Keep at it!


  62. GBA Editor
    Martin Holladay | | #62

    Response to Danny Parker
    Thanks for taking the time for you long, thoughtful comment. I think this dialogue has be very instructive and interesting.

    This time, I'll avoid any temptation to emphasize points of agreement.

    As those familiar with the legacy of Energy Design Update (which I edited for seven years) realize, I have long championed the cause of residential energy research. I have often come to the defense, in print, of the work of researchers like Evan Mills and Danny Parker. All of us involved in the design and construction of superinsulated homes stand on their shoulders. We owe them an incalculable debt.

    Whatever its flaws, the Oregon study bore useful fruit. The surprising findings of the Oregon study, which Michael Blasnik has accurately shared, should have been exciting to any scientist. Scientists love unexpected data. In fact, it sounds as if Danny Parker, Evan Mills, and others in the modeling community responded to the findings exactly as scientists would be expected to respond -- by reviewing their models to see if any algorithms could be improved. That's the way the system is supposed to work. This is all good news.

    The needs of research scientists and residential energy auditors are not the same. A powerful software engine that is capable of hour-by-hour simulations is a wonderful tool for research, and I don't doubt that these tools get more accurate every year. These tools can also be the hidden engines for software used by energy auditors, even when the auditor's version of the software requires only a few inputs.

    That said, one of Michael's most important points -- that not every home needs to be a science project, and that not every energy retrofit job requires modeling -- is an astute observation that should help inform people designing residential energy retrofit programs. I feel confident in saying that Michael Blasnik meant no disrespect to Evan Mills when he made this point; nor did I.

    Finally, both Michael Blasnik and I have clearly and repeatedly stated that energy modeling software is useful — and for some purposes, essential. Let's use it when we need it, and skip the modeling whenever it distracts us from the tasks at hand.

  63. user-943732 | | #63

    response to Danny

    I'm not exactly sure who you are arguing with on many of your points -- I think we agree more than you realize.

    I think we should use the best models we can but when it comes to field applications for energy labeling or retrofit analysis that we need tools that require as few inputs as possible, are easy to use, and provide solid estimates of energy use and retrofit savings. I'm agnostic as to exactly how that is accomplished. Many people have thought that if you ask for many data inputs and run an hourly simulation model then you are guaranteed more accurate results than using some simplified approach involving fewer inputs and perhaps a simpler modeling engine. I think people are realizing that isn't necessarily true.

    I'm not sure where you got the idea that I'm against using computers or detailed modeling or sophisticated algorithms -- that isn't true at all. I'm just aware that in many retrofit programs the audit tools can impose a significant burden in time/cost/focus and still not produce useful results. It's important that the tools are useful and not burdensome.

    It's interesting that you mention tools of the future that learn from the past and assess model adjustments based on derivatives of inputs -- I've been working on these very things. The "learning from the past" part has been a manual process -- I've done many research projects and retrofit program impact evaluations over the past 25 years and have tried to incorporate the lessons learned from that work into improved modeling assumptions and methods. The model calibration part involves using those first derivatives you mention (d_energy use/ d_model inputs) along with an estimated var/covar matrix of input uncertainties to develop a unique solution to the model calibration problem while also being able to give feedback to field people about potential problems with the data they are entering. Like you, I love developing and playing around with this stuff and I love simulations and modeling. But I want to make sure that all of this sophistication can take place in the background and that the tools don't become a burden to use and actually provide useful information. I think we can agree on that as well.

    Let's all keep working to improve the tools we have and keep checking their outputs against the real world. There's lots to be done.

  64. user-943732 | | #64

    response to Albert
    I hope you realize that when Danny is talking about simulation models that would not include PHPP. PHPP is not an hourly simulation model but is truly a spreadsheet. I think that means you are the one arguing in favor of the horse?

    I'm just advocating for either a car that actually works or else a horse if there isn't a working car. around or maybe even walking if my destination is nearby.

  65. albertrooks | | #65

    Well... ok.

    Long thread and I came in late.

    I mis-read a few comments as "downplaying the value of modeling" due to the time and cost associated. That was what I was reacting too. I see that they were meant to temper budgets and not devalue the practice overall.

    When I was thinking of the future in Modeling, I wasn't thinking of the static PHPP. I was imaging what had been presented as the next stage as WUFI 3D: A 3D dynamic model detailing temperature & humidity at optional selected points throughout the wall/roof assembly. Seems like it would detail performance quite well. Now that'd be the car worth pulling out of the garage.

    To me, the PHPP is graceful in it's single dimension, rigorous in it's demand of Therm calcs for bridge areas, it's "ja" or "nein" for airtightness. All in all, a pretty reliable and accurate "horse".

    All great tools for new construction. Retrofits will probably remain tough. Perhaps that's really a guided walk.

  66. XUXfjRJt2K | | #66

    I believe the discussion following Martin’s article has been a healthy one, and may help to clear up various misconceptions.

    No offense was intended by our Comment 56 in response to Martin’s Comment 51. Danny and I discussed the original comment and agreed on the essence a response; Danny was quite busy and asked that I pen the reply. I indeed missed the qualifying term “every” in his third point, for which I apologize; it certainly was not deliberate… There are certainly home energy upgrades that don’t require modeling, or at least the kind of modeling (math) that home performance professionals do. In fact, the consumer version of the Home Energy Saver ( enables laypeople to do those kinds of low-touch assessments with a minimum of time investment, and without having to pay out of pocke for the information.

    The Oregon study wasn’t a particular watershed, honestly. Validation work on the underlying DOE2 engine and improvements to the HES system had actually been ongoing long before then. In fact HES was in the middle of a major upgrade exactly when the Oregon runs (unbeknownst to us) were happening, which was a bit disconcerting.

    From our perspective HES actually came out better, in many respects, in the Oregon study than the other tools (best symmetry of errors around actual values, and better median results by many metrics) and so we weren’t particularly concerned. Our concern, rather, was around deficiencies in the study methodology and analysis, and repeated questionable interpretation of the study’s findings.

    In any case, validation is a highly important and worthwhile pursuit—if done correctly and, ideally, in a way that helps actually improve the models. We’re also encouraged that many of the trends discussed in this thread bode well for smarter and lower-cost ways of bringing good analysis to bear in an increasingly cost-effective manner. In fact, with this very much in mind, DOE is soon to launch the Home Energy Scoring Tool ( for asset rating, which is built on the HES architecture.

  67. NZ8kT4vjFM | | #67

    Intent of the EPS Pilot
    Although I love a good hourly building simulation as much as the next person, divining the merits of different modeling approaches was not the focus of the 2008 study. Energy Trust of Oregon sponsored that research to explore the parameters of a cost effective asset rating program for existing homes. As one of the study's authors and the manager of the field work I would like to clarify a few things.

    The study's two key findings, that a score should based on a representation total energy consumption and that models optimized for fewer inputs could be developed to deliver such a rating, have both shown relevancy over the past few years. In creating the Home Energy Score, DOE created a metric that is less granular than the report proposed, but is consistent in concept. Also models have been improved for this purpose in the subsequent years.

    Does that make it a landmark study? Probably not. Useful for guiding policy and technical development, sure. Was there bias in the study? Not intentionally towards one tool or another that's for sure. Was there measurement error? It was a field study with 5 different auditors, so yes. To minimize this there was very extensive error checking, including follow up phone calls and follow up home visits just to very suspect data points.

    The merits of energy modeling is always a hot button issue, so it is not surprising that the study produced debate about the merits of modeling and different modeling approaches in areas of work far outside the study's focus. It should be noted the study pointed out merits of Home Energy Saver and suggested the improved optimization of its inputs if it were to be used for an asset rating.

    Evan, Danny and others obviously disagree with the study's methodology and analysis, fair enough. I would just caution that those opinions may not be considering the actual framework of the study; when analyzing modeling tools we wanted to determine whether tools were suitable for delivering a cost effective asset rating for homes. That focus determined the comparative analytics used. Were those analysis methods different those used for other research purposes, yes because we were asking different questions.

    On one last note, I would like to point out that Evan and Danny are now working with some of the study data in their current work. The 2008 study did a very thorough (my staff might say excruciatingly thorough) job of collecting and cataloging the Home Energy Saver data. It is doubtful any other study will conduct that level of field data in the near term. Painting the study as technically deficient with a broad brush is not helpful, especially if elements are proving to those very detractors.

    Personally I love energy models, I just don't want to waste unnecessary time creating them. For years I utilized them to determine best practices for building high performing homes. Hourly simulation is great for that, but I didn't/don't feel the need to model every home. My focus has changed from that pursuit to making our existing buildings better and generate a rating that let's a building owner take credit for that improvement, for that purpose we need tools that get it right as quickly as possible.

    We're making better tools. Let's keep it up.

  68. user-1120174 | | #68

    Occam's Razor Meets George Jetson

    Dear Friends,

    I hadn’t checked here in a few days and see that I should have taken a look earlier. I think I need to address several individuals in hopes there are no bad feelings.

    First to David Heslam, we very much appreciate Earth Advantage sharing the Oregon data to us to till again. That has been in progress in recent weeks and it has been useful. It has been useful the same as other data from Wisconsin, North Carolina and Florida. It is also helpful to know that the Oregon data was prepared with great care and knowing that makes addressing model shortcomings all the more important. Thank you.

    To Michael, I would say that we have very similar perspectives. Although this series of commentaries has concerned simulation and modeling, I always believe that measured data is on first and I know you see it that way too. Indeed it is why we are here discussing our ability (and inability) to understand it completely and predict well. Still, I typically find myself nodding to many of the observations you make– particularly the recent one relative to how error from models tend to propagate at the square root of their differences. That makes better prediction tough from the start.

    Indeed, that has been a fundamental finding in work over the last months: unless you can see where simulations are making errors in the end-uses, the prospect of improving predictions is pretty daunting as compensating errors from one “refinement” after another does not necessarily take one straight away to reduced error. There are lots of compensating changes where imprecise input leads to some things helping prediction for an individual case while others taking one further away.

    My main argument is not with you, but rather directed at the conclusion that seemed to emerge from the Oregon study– that the accuracy problems with prediction lies with simulation itself. To go over that again, I believe simulation is the better way to meet the challenges at prediction:

    • Using hour-by-hour simulation provides weather data at fine granularity based on detailed meteorological observation, with respect to outdoor temperature, coincident solar radiation, nighttime long wave irradiance and wind speed. While cooling and heating degree days and information on solar irradiance can be distilled, I like to think that the actual measured weather is important to our models. (Indeed I have been arguing for years that rain should be important to reset the roof temperature when the TMY rain flag shows precipitation). Of course, as Michael has already observed, using these data willy-nilly can be trouble– for instance using airport wind speed at a 10 meter height says little about the wind speeds down at house neutral pressure point and with localized shielding and terrain from the suburban landscape. Otherwise, surface film coefficient on windows are undervalued and infiltration estimates are exaggerated. Indeed, these are some of the improvements that we have been carving into the simulations lately. Makes a difference. Still, I look at detailed weather as a good thing.
    • Simulation models such as DOE-2, TRNSYS and EnergyPlus generally have the most rigorous engineering models in them. That doesn’t mean they are correct, however. As I mentioned earlier in this blog, one of the tenets of simulation– that of a homogenous interior temperature– is very often violated in real houses, particularly older, more poorly insulated ones. As Michael knows, there are no bigger knob for space conditioning models than the interior temperature (thermostat) approximation. Thus, our increased attention to this phenomenon and the collective ‘Ugh’ from everyone recognizing that interior walls and thermostat location are about to become important– at least if you want to predict the savings of insulating a turn of the century (the previous one) brick two-story. There is work there, yet to do.
    • Hour by hour simulation models allow prediction of hourly loads. This becomes more important as more and more utilities move to time based Time of Use (TOU) rates or even Critical Peak Pricing. As PV costs drop below $6/Watt, you’ll see a lot more of that and how that matches up with TOU rates will be important. Same thing for plug-in hybrids: we’ll eventually need to simulate them and how they effect the TOU mix. This move by utilities will only grow in the future since their costs of generation vary with time, season and weather conditions. The competitive nature of the business dictates that they face us with that music eventually. Simulation will be needed.
    • The time required for running an hourly annual simulation is trivial (<4 seconds), compared with the time for input and, particularly of developing input. why not do best calculation possible those precious inputs?

    That said, I do think that simple models have some intrinsic advantages over complex simulation. The key one is parsimony where getting things wrong in a model with a simple engine is less likely than getting something wrong in a very complex model, such as DOE-2, where such an eventuality becomes a near certainty.

    Even so, the engineering model needs to be as fully complex as the situation demands it, but no more. It’s a difficult edge that evokes Occam’s Razor and allows me to bring up Einstein again: "Everything should be kept as simple as possible,” he said, “but no simpler."

    As I have made clear, I believe the effort to get the complex models right and use best quality weather data is worth it to the extent that some phenomenon can otherwise be underdetermined. In any case, the national labs and FSEC have been policing each other as we do pretty thorough examination of comparative models and use that process to illuminate differences.

    Yes, we get carried away at times (a science project), but often back off to what works well enough. There is the BESTEST suite of simulation cases which allows one to see how simulations stack up against each other for pre-arranged cases. However, BESTEST is no panacea either. How can it help if all the models are in error? That has already been productive to correct some real differences in simulating windows– something being corrected in BEopt (made clear by differences in the DOE-2.2 and EnergyPlus implementations). Similarly, some inadequacies in the EnergyGauge simulation of heat pumps has been corrected (strip heat is commonly engaged when the reversing valve is activated and defrost is in progress). A variety of improvements have been made in HESPro– particularly for modeling machines, basements and air infiltration. Gotta get those things right– particularly if best assumptions were not used before.

    Recently, we have been able to examine the predictions of HES against natural gas consumption for space and water heating in a collective sample of 450 homes around the U.S. While, we still have appreciable scatter (yes, a lot), we are spot on for the averages. Turns out that electricity is another matter– none of the models do that well, including SIMPLE, and for reasons that aren’t immediately apparent. We’re looking into that. Still, I would hope that if Michael runs HES today against his home, it wouldn’t still be high by 40%!

    A comment from just about everyone, as Martin clearly reiterated, is that auditors should not be overtaxed in providing information to a model. And that is a fundamental point we agree on.

    However, this is one key area where Evan and I are trying to address a misconception. While, one may believe that something like HESPro requires a plethora of inputs, that is not true. It can be run with a very abbreviated list of inputs– as simple as any other model. The key limitation, is that USERS have not been given any guidance about what those “most important” inputs are.

    And perhaps we are guilty of not helping with that as much as we could. “Quick inputs” in HES was one answer; but many users choose Detailed mode. Having 150 inputs may suggest all of them need to be filled out, but that is not the case. Not all the inputs are of equal import. And even knowing that only some need to be addressed how is a lay person to know which ones? How to span that gap? Leaving users to their own devices only invites sub-par performance and frustration.

    HESscore is one answer to that process– a truncated list of 30-odd inputs based on “expert opinion.” But while adequate and consistent, that fixed series of inputs, however simple, may not provide the most accurate result.

    I sounds like Michael B. and I are on the trail of the same thing: using the heuristic smarts of computers to help find the right inputs to demand from auditors based on what is learned from past performance of the system in predicting future loads. Of course, they would not always be the same inputs necessarily. The problem, of course, is that auditors and home owners typically have a limited attention. We need to use their attention to maximum benefit before they glaze over or exceed the audit budget.

    Based on Evan’s priorities, we are working on that over the next year for HESPro. We’ll see what we manage. (One trick is obtain two years of billing data; use the first half to tune the model and then see how well it can predict the recent year– Delphi method in action). Cluster analysis might then be used to help sort out the most important groups and their common critical inputs. Or that’s my idea.

    It also turns out that end-uses loads are very important, as mentioned in the previous blog. This is important to help understand where prediction error is coming from. Homes are not just heating and cooling; they are water heating (where knowing gallons per day is vitally important), laundry (washer and dryer that are very sensitive to occupancy), refrigeration (guess what? second refrigerators are often way different from the first), fans and blowers, cooking, lighting and entertainment and plug loads. Lots of places for error.

    In fact, it is worse than first blush when it comes to predicting retrofit measure savings. For instance, a model that predicts monthly energy right can’t be known to be as reliable for predicting the savings of a heat pump water heater as one that has been subjected to see how well the models are predicting the daily hot water gallons without bias. (By the way, speaking of uncertainty, hot water gallons appears particularly variable even given knowledge of occupants and other fundamental factors). Same for predicting the savings of an air conditioner: better be predicting space cooling well regardless of how close you are on the monthly utility bill. Compensating errors don’t help much then.

    Unfortunately, the cases where we have the above end-use information is spotty. But we are in search of it and have found some– that data being uniquely valuable in the quest for the Holy Grail of improved accuracy.

    Will we do better? For sure.

    Yet, as I mentioned, computers and computing power and the ability of machines to help us understand their own limitations and ours may prove invaluable. Such expert-system applications may play an ever greater role in improving prediction while reducing the onerous nature of audits and lengthy forms. It’s my conviction that simulation with a forward-chaining inference systems of asking the right questions will play an important role in that process. The computer can refine the prediction as the auditor or homeowner provides data and then seek more where it is most needed. It should be able to ask the most important questions first.

    That won’t come to energy analyst George Jetson right away, but the next few years could see many improvements. I have been pleased to be able to help with these things, at least in a small way, and I appreciate the efforts of others, even when we do not see eye-to-eye.

    Everyone agrees we are all trying to improve things. I’ll add that we should be turned out to pasture if we are not.

    Danny Parker

  69. robinmcc | | #69

    Yes, but....
    I do have to agree that today's energy models remind me of the scene in Animal House where John Belushi carefully measures the windshield of the Cadillac...then smashes it out with a sledge hammer. On the other hand, rocket scientists started with the same sort of inaccurate models but eventually got to the moon. These models may not weight the variables appropriately and we know that we can't model human behavior, but we still want to aspire to having models for heat and moisture flow that approach the reliability of structural force models.

    Another reason to use models is strictly cosmetic. If you notice, women's shampoo is often advertised as having some ingredient with a long scientific name like Importantene. The name means nothing, but it does sell shampoo. Likewise, as an instructor, it really helps to have an inscrutable model when you are trying to teach long time builders that fiberglass isn't state of the art anymore.

  70. GBA Editor
    Martin Holladay | | #70

    Great discussion...
    It's a pleasure to come back from a week's vacation and find a series of stimulating, thoughtful posts. Thanks to everyone contributing to this discussion.

  71. wjrobinson | | #71

    Simple. Danny Parker? Simple
    Simple Danny Parker? Not your post at least.

    Simple is moving taxes from good to bad.

    Raise taxes on fossil fuel, while lowering taxes on green sustainable income.
    Mandate solar, outlaw discontinuous insulation.
    Deliver only so many BTUs of enery per residence. You build a 1000sqft or 10,000 or 100,000sqft and you are given the same amount or energy to work with per year. So if you have a huge home, you also surely can afford to install enough solar to cover your needs.

    My plan makes modeling work because one of the biggest problems with models is the lack of control of the "knob turning" homeowners. With the limited energy delivery scheme it becomes very personal for a homeowner to control his "knob" turning or pay for his lack of knob control not the community, the planet.

  72. Tedkidd | | #72

    GREAT article!

    "So why do energy-efficiency programs almost always overestimate anticipated savings? The main culprit, Blasnik said, is not the takeback (or rebound) effect. Citing data from researchers who looked into the question, Blasnik noted, “People don’t turn up the thermostat after weatherization work. References to the takeback effect are mostly attempts to scapegoat the occupants for the energy model deficiencies.”

    But following the data too rigidly may have lead to obvious conclusions, and sometimes obvious conclusions are incorrect. I've found this to be false:

    "most energy models do a poor job of predicting actual energy use, especially for older houses."

    The real problem is data input. If you do a crappy job inputting your data, then don't even bother to true to actual consumption (anybody reconcile their bank account?), of course you get gross savings overestimates.

    Add energy program minimum savings, and sales people that consciously or unconsciously want reports to show more savings, cover it all with no accountability for accuracy, and I think blaming the software seems to me jumping to the easy conclusion or "scapegoating" also.

    I'd CHANGE THIS: "References to the takeback effect are mostly attempts to scapegoat the occupants for the energy model deficiencies.”

    to THIS: "References to the takeback effect are mostly attempts to scapegoat the occupants for the energy MODELLING deficiencies.”

    Is it any surprise modelling sucks? All the classes pooh pooh it. Heck, there is no dedicated certification yet it's arguably the most complicated and important piece of the process. It's nearly universally treated as something to be rushed through. With all these pressures against accuracy, where is there any counterbalance? There is none.

  73. heinblod | | #73

    Retrofit studies are consistent: projected savings are overestim
    Here a Cambridge study about the so-called "prebound effect", covering Germany, the Uk, Belgium and France:

    See bottom of the page for the full paper.

  74. GBA Editor
    Martin Holladay | | #74

    Response to Hein Bloed
    Thanks for the link. The European study reinforces Blasnik's conclusion (and my reporting): “Blasnik cited five studies that found that the measured savings from retrofit work equal 50% to 70% of projected savings. ‘The projected savings are always higher than the actual savings,’ said Blasnik, ‘whether you are talking about insulation retrofit work, air sealing, or lightbulb swaps.’ ... Energy-efficiency programs almost always overestimate anticipated savings ....”

    One of the authors of the European study, Dr Minna Sunikka-Blank, noted, "This challenges the prevailing view that large cuts in energy consumption can be achieved by focusing purely on technical solutions, such as retrofitting homes. In some cases, doing so may bring only half the expected savings, perhaps less."

  75. DellStator | | #75

    $ + Hubris = GIGO
    Accurate energy modeling is readily achievable, if you remember to KISS ....your S.O. every morning when you leave for work --- no the other KISS, Keep It Stupidly Simple.
    Energy savings from retrofit insulation, EASILY done, on a napkin, waiting for your lunch.
    then relax, enjoy your BLT and do the window replacement while waiting for the check.
    Hubris - Yes, rather than trying for an all seeing and all knowing BEM, modeling smaller, simpler and FEWER the components will give you more reliable information, that is, get the inputs right, remembering all you need to consider, get data, make thoughtfull guesstimates, etc.
    Money - Software is supposed to save money making it possible to acheive the improbable for nothing? No, it's just a tool, one that requires KNOWLEDGE, knowing the bulidng type / occupancy, software and the data, to create reliable models. All but the rarest of clients building a state of the art eco-monument, will be able / willing to pay for knowledable staff to accurately collect / estimate the literally thousands of data points necessary for a reliable whole building energy model.

    As others said, "Trueing" models to yearlyt energy bills and climate data (adjusted for local micro climate) etc. is MANDATORY for a reliable BEM. Yet new buildings don't have historical energy use data. VERY general info is now used for this, while what we need is the energy data for the building down the block - which means talking them out of it / talking them into doing an energy audit of their building (see how I turned that "problem" / "cost" into an opportunity??? and, you spread the cost of collecting and massaging climatic data over two (or more - why not try for even more synergistic savings, go down the block offering energy audits at "discount" to everyone on the street? it's what driveway contractors do, must work, most of them live better than I do).
    For reliability, BEM needs validated open source data summarized by individual and grouped buildings / occupancies listing major data points, size, shape, orientation, occupancy, users, energy per area / user / system, etc for input to short cut the expensive / inaccurate data collection process with known good data.
    A non-trivial problem.
    Anyone good at grant writing?
    Team with your Alma-Mater, get the A/E depts going and some luckly professor published.

  76. Nate Adams | | #76

    A synopsis of Parker's presentation
    If it is helpful, this can be read in a few minutes with what I found most poignant, which doesn't mean I'm interpreting as Danny, Evan, et al would like.

    No one cared about accuracy in many models, as indicated by Blasnik's presentation that the error 'Was almost entirely from the pre-retrofit usage estimate'. (Page 9, top left slide of presentation below from Summer Camp.) Truing the models to actual consumption would make models accurate, so why aren't we doing that before claiming models are junk?

  77. sgbotsford | | #77

    Use the utility bills
    Seems to me that the best sanity check would be to ask them for the last year's utility bills, and from the weather office, last year's degree heating/cooling days. From this you can calculate the true performance overall of the envelope.

  78. vap0rtranz | | #78

    Open Source vs. free
    To AJ Builder about his comment on Open source modelling software.

    Open Source does not equate to free. There are open source licensed softwares that people pay or donate. If you mean free as in fiat money, then the proper term is "freeware".

    Anyways, the good news is there are OSS (OpenSource Software) for BIM (Building Information Modelling). Here's the latest list:

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