In an earlier post
("Changing Perceptions about the true cost of energy and water conservation" - July 16, 2011) I asked how can we change the perception of the value of energy and water conservation if the money saved can only be shown to have been saved by showing that the reality of the situation is that they would have spent more if they had not had the energy or water conservation measure installed?
Our most recent experience with this has been with the pilot project where we have been attempting to measure the energy savings associated with spray foaming half of the roof deck with open cell spray foam and creating an unvented attic while leaving the other half vented and exposed to the hot, humid weather (Although I would add that when I've measured the humidity of both the vented and unvented attic they were both at around 60% relative humidity). A simple analysis of the utility bills from the same months last year would show that the electric cost increased by $389.80 for both April and May. This has resulted in a bit of a conundrum in that we are not trying to show that there were energy savings but it was more of an exercise to determine if we can show energy savings.
Thankfully this is a common problem in the energy management field and there is a great resource located at
http://www.degreedays.net/ and
http://www.abraxasenergy.com/weather-normalization/. To keep it short the end result has been that to accurately estimate energy savings one needs several things:
1. Accurate utility information concerning kilowatt consumption and electric costs (ideally weekly instead of monthly utility data)
2. A determination of the baseline temperature that the building has to heat to in the winter, taking into account, albeit indirectly, heat gain from individuals, electronics, solar radiation, etc..., and the baseline temperature that the building has to cool to in the summer taking into account, indirectly, the effects of shading, etc... This baseline temperature is determined by comparing the average outdoor temperature per month to the number of kilowatt hours used each month and finding the intersection of three distinct lines in a temperate climate where we are working (as far as I can tell from the resources above).
3. Using this baseline information one can calculate the number of heating and cooling degree days which can be used to perform a linear regression on the number of kilowatt hours to estimate how the heating and cooling loads of the building, as a function of temperature, affect electrical consumption.
Below are the tables showing energy consumption by kilowatt hours and electric costs for 2009 to 2011.
As you can see above the most difficult thing has been finding complete utility bill information and obtaining the exact costs per month considering the actual rate for energy consumption as several of the earlier months in 2009 are missing. Based on the information presented above the average cost is $1.17 per kilowatt hour.
Using this information I created a scatterplot comparing the number of kilowatt hours per month by the average monthly outdoor temperature. This is presented in the figure below:
For a general overview of calculating the baseline temperature of a building or the building's balance point please see the following website
http://www.abraxasenergy.com/weather-normalization/. A building's baseline temperature or balance point indicates at what temperature the building no longer has to heat or cool taking into account the internal heat gain (in winter) and shading (in summer) for example. Based on the data available it was determined that the baseline temperature during the heating season, or the temperature the building needed to maintain during the heating season, was 60 degrees Fahrenheit and the baseline temperature during the cooling season was 71 degrees Fahrenheit. What is interesting is that since we are in a temperate climate there is no predominance for heating or cooling so it was necessary to obtain both the heating and cooling degree days to analyze the energy bills. Using this information I obtained the heating and cooling degree days using this baseline temperature from January 2009 to June 2011. Below are graphs for the heating and cooling degree days.
What is interesting is to compare how the number of heating and cooling degree days per month varied each year depending on the local weather conditions. This is the primary reason why one should use heating and cooling degree days that have been calculated relative to the baseline temperature to compare how energy conservation projects measure up to calculate estimated energy savings. However this is not the only indication of increased energy consumption that should be accounted for and may also include energy consumption associated with increased productivity, such as an increased number of guests at a hotel.
The next step is to use this information to run a linear regression which will be in part two of "Are Estimated Energy Savings Really Savings?"
