In an earlier post I had tried out a few ways to figure out if our energy-saving efforts with respect to natural gas use were paying off; I did a few bar graphs of therms per day, per heating degree day, rolling yearly averages, etc. I knew that I needed to normalize for the weather using Heating Degree Days, since natural gas is our primary heating fuel, and I probably needed to find a way to separate space heating from water heating, which have different conservation methods, and which may or may not be weather dependent.
I came across this article, Degree Days – Handle with Care! (run by the same folks who provide www.degreedays.net, a great source of free HDD data) which listed a lot of the issues involved, some of which I had thought of, and some of which I had not, including:
- Separating out non-space-heating use (water, cooking, etc)
- Getting the right base temperature for the building in question
- Using proper degree day data for the actual monitoring period
- Differences in occupant behavior over the time period
But the other thing that caught my eye was the idea to do a scatter plot of HDD vs. heating therms used, and doing a linear regression; the slope should give you an idea of your average therms per heating degree day.
The graph above is my attempt at this; I found some old data from 2004-2005, which was before we did any insulation work, and I have more recent data for the past 4 or 5 years as well, so I have a decent data set. The graph above does not attempt to factor out water heater use, or to perfectly match HDD (which I actually obtained from the Minnesota Climatology Working Group website, as monthly data) to the precise billing period (which can vary from 27 to 35 days). It simply graphs therms vs. heating degree days, by heating season, for any months which had over 200 HDD. But even with that basic, raw data, I still think it shows some interesting trends.
(edit: if you click above, the live graph now has all datapoints for all months; this more accurately shows the base gas load at the Y intercept. It is also more accurately matched to the actual HDD for the billing period.)
Edit: After a brief discussion with the nice folks at degreedays.net / energylens.com, I have a few more ideas on doing a better analysis. Hopefully will have some spare time over the holidays to try out some other things, and do another post.