You cannot design a net zero building without a detailed Energy Model to accompany you throughout the design process. Nor can you design a geothermal system without a detailed energy model providing a clear picture of the loads extracted and rejected from the ground. You also need an energy model for the LEED submission process. A casual observer might naturally assume that this energy model is always done by the same person. Unfortunately, this is almost never the case. This is our first project where the owner has taken advantage of this work “crossover” and allowed one company to do all three tasks with one model. For the owner and project team, this is a huge benefit in terms of cost, time and model accuracy. Having to coordinate all the building details (occupancy schedules, equipment, HVAC, electrical, etc.) is tough enough without having to do it three times, with three different groups.
Each task—net zero design, geothermal field sizing, and LEED submission—has different output requirements and risks associated with them. The main goal of the Net Zero design is not just to optimize and reduce the energy consumption of the building, but also to size the photovoltaic (PV) system. This means that unlike the LEED model, which is used mostly to “count points”, the energy model for the net zero design has very real consequences on the success of the project. Plus or minus 10% could mean the difference between Net Zero and a Living Building Challenge accreditation, or neither of those. We should also not forget the cost implications of the size of the PV system; a 10% error could mean $20,000-$30,000 in PV costs. Similarly, the size and cost of the geothermal system is based on the results of the energy model. The loads exported to the geothermal design software directly influence not only the size and cost of the geothermal system, but also the seasonal efficiency from year 1 to year 50.
Our goal became clear. The energy model needs to be accurate enough to cover every possible electrical demand in the building so that we can size the PV system, without being so conservative that we needlessly oversize it. The energy model also needs to be accurate enough to adequately size the geothermal system without being so conservative that we needlessly increase the number of boreholes.
This is where it got interesting. While the goal for sizing the PV and geothermal system are very similar, the inputs that make one or the other more or less conservative aren’t the same. For example, let’s consider the impact of increasing electrical loads (from lighting, monitors, computers, etc.). Increasing the assumed electrical load in any space obviously increases the electrical consumption and; therefore, the size of the PV system. However, the increased heat gain in the space offsets heating loads in the winter, which tends to decrease the size of the geothermal system, or at least change the load balance of the field (making it more cooling dominant). This raised some serious questions in our minds. Does it make sense to use the same model for designing both systems? Should we separate the files and fine-tune them separately? Is increasing detail for the sake of one system bad for the other? By being conservative for the PV system, do we remove the safety factors in the design of the geothermal system?
The answer to these questions would be simple if we knew exactly what would happen in the building, but energy modelling is not an exact science as it is impossible to predict human behaviour. The energy model is only as accurate as our assumptions. Will they use the monitor 2 hours a day in the meeting room? How often will they turn on the lights in this storage room? How many people will be in the exercise room, and for how long? The answers to these questions are never exact and so we must make educated guesses. If we were designing only the geothermal system, or only the PV system, we might make guesses that are slightly more conservative and increase our system’s safety factor. The challenge lies in designing for both systems. This requires much more thought about the influence each assumption has on the design of both systems. It also requires running many more options to see how each system is influenced, then picking the assumption that is the best for both systems. In the end, however, the goal is to make the most realistic assumption while providing flexibility in case we are wrong. This project reminded us that the entire process is a juggling act between accurate energy modeling and proper design.
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