The convergence of several issues, and AESP’s Summer Conference, Technology – The Great Game Changer, gave me no choice. I have to do this – talk about the many issues involving building energy models, which are used for everything from single family homes to high rise office and apartment buildings. I prefer the term simulation over model for these detailed and very complex computer models. In this post, Simulation = Energy Model.
A Good Excuse for a Simulation
The best use of an energy simulator is when there is nothing else to work with. When is there nothing to work with? When there is no building – new construction.
A subset of new construction includes a deep retrofit, or gut and rebuild, primarily for space heating and cooling. This is essentially new construction within an existing building shell where the energy consumption going forward will look nothing like trailing usage.
A Usually Poor Excuse for a Simulation
Using energy simulators for existing buildings can be very costly, primarily because simulators “assume” buildings work like a modern-day James Bond Aston Martin, whereas actual buildings work like Henry Hill’s 1979 Cadillac Coupe de Ville – without a tune-up since. And believe me, those cars needed tune-ups. Just look at that crisp handling!
Efficiency measures for an existing building may include control strategies, adding a few variable frequency drives, some lighting upgrades, and maybe converting some systems to digital controls. In 95% of cases, such measures are just as accurately, and more quickly, evaluated using other energy models, such as those in spreadsheets.
Some audit tools have graphical interfaces to provide inputs to old-school simulation engines like DOE-2. These may be good solutions for armies of auditors that need to be kept between the guard rails, but to an energy expert, they are cumbersome to use.
A classic example is a pump running at full speed with an outlet throttling valve pinching off flow to only 20% of capacity. The expert who knows pump curves and system curves can determine savings more accurately and faster with a spreadsheet than burrowing into an energy simulator to finagle it to provide what is needed. In fact, using a simulator in this situation would include these steps:
- Determine savings with curves and a spreadsheet
- Reverse engineer the simulator to provide the spreadsheet’s answer
Make sense? No. It makes for waste; of time and money.
Blame the Simulator
Perhaps I’m a little sensitive to shooting the engineer and simulator he uses when things don’t turn out right. The following is from the famous, or infamous, E2e Working Paper that evaluated Michigan’s federal low income impacts.
Conventional wisdom suggests that energy efficiency (EE) policies are beneficial because they induce investments that pay for themselves and lead to emissions reductions. However, this belief is primarily based on projections from engineering models.
Stupid engineering models! The report goes on to show that savings are only half of what the stupid engineering models predicted.
Stakeholders, including the modeler, need to understand simulator results are only as good as the data going into the model. What happens between simulation and measured results (construction) is the problem.
Want a perfect example? Great!
A couple months back I was considering evaluation approaches for residential heating measures; specifically natural gas furnaces and boilers. The buyer for the project wanted to evaluate impacts six ways to Sunday – very expensive, and not in their best interest.
The impetus for the six-ways approach was that several other evaluations on the same technologies showed poor savings realization – about 70% on average, plus or minus a few percentage points. As a result, they asked for billing regressions, simulations, and even metering gas flow to the units. A king’s ransom!
With 99% certainty, the “problem” is the equipment is oversized by about 40-50%, on average. That is, 1÷0.7. It hardly matters what the building type is. Design engineers and/or contractors are going to use belt and suspenders with a safety factor of 1.5 built in – just in case a wall falls off the building and it gets a little drafty. This is NOT a simulator problem.
Modeling Residential Measures
Residential behavior and energy use is radically different from one house to the next. For example, how many hours is a typical residential light bulb used? I would love to see a survey of homeowner guesses. The answer is 2.8 hours per day, plus or minus a few tenths of an hour, on average. As new measures emerge, product developers should use as much applicable secondary data as possible, followed by primary data collection in a pilot phase.
The only way to assess heating and cooling energy use for residential, regardless of the measure affecting it, is billing regressions. If it’s a unit replacement, like the lightbulb, the savings will reflect 70% plus or minus a few ticks of the unit’s capacity (described above). Seventy percent is analogous to the 2.8 hours.
Like the variable frequency drive example above, simulations for these measures are a waste of time. You would use regression to determine usage and savings, and then reverse engineer the model. What is the point?
- Simulations are useful almost exclusively for commercial new construction.
- Use a modern simulator, NOT DOE-2 – a topic for another day.