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Energy Modeling

Bradley and Emma led the team's energy modeling efforts.

Our overarching goals were to determine the optimal insulation depth and material and the window orientation that would best maximize human comfort while minimizing energy load and, thus, cost and potential emissions. First, the group did preliminary research into heat load modeling of houses and how that could apply to our project. Emma and Bradley both had experience with sustainable design, so prior class experience and conversations with Professors were also both utilized.

During the first few weeks, we also identified key interdependencies between energy modeling and other groups. We were in frequent communication with both the Envelope/Exterior team and the Interior team, and occasional communication was required with energy systems and green building. Notifying these groups and reaching out to them for information was paramount for our group's ability to accurately and helpfully model the heat load of different designs.

Heat load: the total heat (usually in BTU, in the US) lost via conduction through the building's envelope and through air infiltration (leaks and ventilation) over some period of time, usually one year. It's calculated by:

Heat load

Heating Degree Day (HDD): this metric tells you the number of days that heating was required at a particular location, and how much heating was needed. Usually, this number is given on a per month or per year basis for a certain point, and can be referenced at different desired internal temperatures. For example, a 30˚F day contributes 30 HDDs when referenced at 60˚F (1 day • [60˚F - 30˚F]), or 45 HDDs when referenced at 75˚F. For our analyses, we decided to heat the interior of our model to 60˚F.

After completing background research, the group started with initial building heat load modeling using excel. This included a few assumptions: the insulation value of the roof, and the insulation value of the floor.

Figure 1
Figure 1: Heat Loss (different from heat load!) with an R-40 Insulated Roof. This figure shows that as more insulation is added to the walls, the total heat loss decreases. However, there are diminishing returns as the wall insulation level increases.

After observing the impact of increased insulation of the walls on the overall heat load of the building, we analyzed the impact of the roof insulation:

Figure 2
Figure 2: Building Heat Loss of Differently Insulated Roofs. This plot shows that, as expected, adding more insulation to the roof decreases the heat loss through the envelope.

The Exterior/Envelope group gave us a list of four insulation options they were considering to model. Working with that group, we developed a plot to assess the cost of each type of insulation for any given wall R-value (insulation level).

Figure 3
Figure 3: Comparing Wall Insulation Costs. Blown Havelock Wool is the cheapest option per on a cost per R-value basis of the four options plotted here.

 

Finally, we included all variables we wanted to consider when choosing an R-value: the implementation cost (price of the insulation), the lifetime energy cost, and the cost of the floor space. The implementation cost is plotted above. To calculate the lifetime energy cost we multiplied the average daily heat loss (BTU/˚F•day) by the annual number of heating degree days (HDD/yr) in the Second College Grant by the building's estimated 50 year lifespan. Finally, the cost of floor space was assessed in conjunction with the interior group. The "cost" of each square foot of floor space taken up by the wall insulation progressively increases exponentially in our model since the less space there is, the more valuable it becomes. The sum of the costs of these three components (Figure 4), plus the cost of the roof insulation (Figure 5) gives the total cost, which was the value we ultimately aimed to minimize.This plot shows that to minimize the total cost, the R-value of the wall should be 30-35. The Figure 5 plot was made with the assumptions listed in the subtitle.

Figure 4
Figure 4: Lifetime Cost vs Wall R-value. This plot demonstrates the financial tradeoffs of insulation over a building's lifetime: the more insulation you have, the less you'll need to pay to heat the building, but eventually the energy savings provided by the added insulation don't pay for the insulation cost over the building's lifetime.

 

Figure 5
Figure 5: Lifetime Cost Optimization. For any roof R-value, the lifetime cost is optimized at wall R-values around R-30 to R-35. With roofs, it's generally a good idea to install as much insulation as possible.

Next we took roof and floor insulation into account. Based on our construction designs and the way the existing TRS structure was built, it makes sense for these two surfaces to be similarly insulated. In a normal house, it's generally a good idea to insulate the ceiling/attic as much as possible. However, we didn't want to take up too much headspace with the insulation because of the small size of the TRS.

Figure 6
Figure 6: Lifetime Cost for Roof and Floor R-values. This plot analyses the impact of the floor and roof insulation on the lifetime costs, which is again optimized around R-30 to R-35, if the roof and floor are equally insulated.

We found that optimized lifetime costs for the floor and ceiling occur around R-30 to R-35. Luckily, these insulation levels wouldn't require extreme space, so it was an easy choice to decide that we'd insulate the floor and ceiling within that range.

As mentioned on the Energy Systems page, we considered a number of options for heating. Part of our sustainable design goals was minimizing greenhouse gas emissions. The emissions from a wood stove are a bit of a gray area as far as sustainability goes, since part of their impact depends on whether or not the wood was sustainably grown and harvested, where it came from, and whether or not it's scraps. Regardless of this, burning biomass (wood) does emit carbon, and in our decision-making process, we wanted to account for that. So, we calculated the amount of wood that the stove would require under different use scenarios in the winter, and from that calculated the carbon that would be emitted in burning it.

Figure 7
Figure 7 Monthly CO2 Emissions with Wood Stove Heating. Originally, we thought we would heat the TRS with a wood stove. This chart shows the emissions, broken down by month, over the period of one year, under different use scenarios.

 

Figure 8
Table 1: Monthly CO2 Emissions. This table presents the same data as Figure 7.

Even though the CO2 emissions from the wood stove are relatively small, we weren't happy with the building emitting any carbon, plus the stove would have been expensive, labor-intensive to operate, difficult to control, and present potential air quality issues. The one positive we could see was that the stove uses no electricity, so eliminates any of the risks associated with relying on the solar panels. We decided that the cons of the stove outweighed this benefit, and we were convinced to not go with the wood stove option.

We had previously looked into heat pumps, which are a champion of super efficient building design, but they were all for much larger spaces than the TRS, and didn't make sense financially– their electricity loads would require us to significantly upsize our PV system. So, we turned to electrical resistance heating, and calculated the size of the system we would need and its cost under different insulation scenarios.

electric heating system
Figure 8: Electric Heating System vs Wall R-value. This plot shows the required heating system components to meet the heat load demand of different wall R-values. Around R-33 to R-34 is the threshold for both the number of heaters and batteries.

 

electric heating system cost
Figure 9: Electric Heating System Cost. The downsizing of the heating system is reflected in the system's cost. Again, R-34 walls provide the smallest system cost for 3 resistance heaters, 2 solar panels, and 2 batteries.

 

Our conservative heat load calculations told us that on the worst-case day in the winter (0˚F), 3-4 small, 200 W, 682 BTU/hr heaters would be required to heat the building to 60˚F with 3 occupants if the walls were insulated to R-30 to R-35. These heaters would require 2 solar panels (which is what we had budgeted for) and be able to run for approximately 2 days on two fully charged batteries (also part of the plan). Happy with this result, our modeling confirmed that R-34 was ideal for our walls and our zero operating emissions goal.