The need to “normalize” for weather arises very often. For example, you have a year or two of utility bills for a facility, you plan on improving the energy efficiency of the facility, and you need to estimate what the energy savings will be in the future. One challenge is that the past energy consumption is determined not just by the equipment at the facility, but by the variations in the weather experienced by the facility.
What if the winter covered by the utility bills was especially cold, and as a consequence gas consumption was higher than typical? Basing your estimates of savings on a single year, without “normalizing” for weather, or explicitly adjusting the consumption to reflect typical weather conditions, will cause you to overestimate the typical savings in the future.
Normalizing for weather is a good idea whenever an accurate understanding of the current energy consumption of a facility (a “baseline”) is needed; otherwise, as suggested in the previous example, estimates of future savings arising from improvements to the existing facility may be too high or too low, and consequently inferences that a proposed improvement is cost-effective may not turn out to be correct (or, conversely, a truly cost-effective opportunity may be missed).
The need to normalize may also appear in energy production projects. For example, a photovoltaic system might produce more electricity in one year than in the previous year. Is this merely because there was more sunshine in the second year? If so, did this additional sunshine hide deterioration in the system operation?
Sometimes normalizing for weather is not merely a good idea, but rather a requirement of a client or a utility or government funding program. For example, I recently conducted a study for a client who was seeking funding from the Federation of Canadian Municipalities (FCM). The client needed to show how much connecting his building to a district heating system would reduce overall natural gas consumption (and thereby greenhouse gas emissions). The FCM program stipulated that any study had to first normalize past energy consumption for variation in the weather, and then project savings into the future based on typical weather.
Normalizing for weather is, in principal, straight forward: you “fit” a statistical model (i.e., an equation) that relates you consumption data (e.g., utility bill consumption) to one or more variables that you think exercise an influence on consumption (e.g., heating or cooling degree days). When “fitting” the model to the data, you adjust the coefficients of the equation until sum of squared differences between the actual consumption data and the modeled consumption data is minimized. Often a linear equation is used for the statistical model, and the process is called “linear regression”.
I’ve superimposed a straight line on the scatterplot to make it evident that there is a linear relationship between the fuel consumption and the heating degree days. That is, I should be able to estimate with some accuracy the fuel consumption using an equation of the form[1]:
This equation has the right form, but what should I use for the coefficients a and b? A common approach is to select a and b in such a way as to minimize the “sum of squared errors”, or SSE. To do this manually, I start out with a guess for these coefficients, and then I use this equation to estimate the fuel consumption for each billing period. I then compare these estimates with the actual fuel consumption for each billing period. If I square the difference of the two and sum over all billing periods, I’ll have the SSE. This is a measure of how well my choice of coefficients fits this equation to the data; I adjust the coefficients until the SSE is as small as I can make it (unless the line passes through every data point exactly, the SSE will not go to zero).
fuel consumption = a * HDD + b
Then I’ve got my equation. For the data from the example above, it would be:
fuel consumption = 0.36 * 20 – 0.56
I can then use this equation to estimate the gas consumption based on the heating degree days. So, for example, imagine that for the location of this building, a typical month of March will have 620 heating degree days (°C·day). That works out to 20 heating degree days per day. If I wanted to know what the facility’s gas consumption in a typical March would be, I’d plug this into the equation:
fuel consumption = 0.36 * HDD – 0.56
This would tell me that on an average March day, I’d require 6.6 GJ of gas, so over the whole month I’d consume around 206 GJ of gas. To determine the gas consumption in a typical year, I do this same exercise for each month’s typical number of heating degree days.
Normalizing for Weather Using RETScreen® Plus
While this normalization can be done using a spreadsheet, my tool of preference is RETScreen® Plus, a sister program to the better known but completely different RETScreen® 4. (Both tools are available for download, for free, from the Government of Canada: www.RETScreen.net).
RETScreen® Plus is designed precisely for this type of exercise (as well as much more in-depth analyses to be discussed in later articles), and consequently much quicker and (less error-prone) than doing the manual exercise outlined above. The main program features that make it quicker and easier than the manual exercise are:
1) Rapid access to up-to-date daily weather data for locations across the globe
2) Tools for combining and regrouping data sets on different time bases.
3) Automatic fitting of equations
4) Optimization of the heating degree day reference temperature
Source: HeatSpring Magazine
