How to determine HDD's and "savings" from furnace upgrade

Last fall I upgraded my furnace from a 88,000BTU 80% to a 44,000BTU 90%. My gas bills are at record highs, although I'm sure some of it can be attributed to the record cold weather we've been having in Moore Oklahoma. I've talked to friends/neighbours/co-workers and they have said theirs have "gone up some" but won't give specifics.

Where do you find HDD data and how to you calculate the increased "load" from colder weather? I downloaded the HDD file using a 60 degree base temperature. Heat doesn't run when it's above 60 outdoors.

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Hi Bob,

from a link in my other thread I caught this quote:

On page 8-14

"For ease of processing and of meeting data requirements, the industry standard for many years was to use a fixed 65° F for both heating and cooling degree-day bases. However, actual and normal hourly weather data are easily available now, providing flexibility in the choice of degree-day bases.

In general, a degree-day base of 60° F for heating and of 70° F for cooling usually provide better fits than a base of 65° F."


Good find, explains why 65 was always used as a "compromise" base temp. Base temp varies considerably based on solar gain/wind chill. Using average daily temp certainly makes more sense than high/low for a particular day. I'm still liking the "monthly average temp" method.

We just got our latest meter reading, 8.59DTH March 12 meter read date. Use is again consistent with weather.

Here's a reply received when I shared Bob's conundrum with a pro I contacted at the Canadian Centre for Housing Technology:

Hi Steve,

A long time ago, I worked as a housing engineering energy consultant and found out enough about Heating Degree-Days (HDD) to know they are very tricky to use, especially on a monthly basis.

There are so many other factors that affect house consumption in addition to the ones roughly captured by HDD, that you can’t trust that index for a detailed monthly analysis of heat consumption. Some of these other factors include solar gains, internal electrical gains from activity, heat gain from people, wind (though not wind-chill as some of your discussion indicates), and thermal mass as per your discussions with Marianne below. For example, HDD simply truncates data involving temperatures that are hotter than its baseline (for example 65F), so that it will over-predict heating loads on days with warmer daytime temperatures and cold nights. The mass effect carries the house through these periods in reality. The furnace can be shut-off by the owner because they know the heating season is over, but the HDD keeps adding up.

With the large amount of data we collect for our houses, we have never looked at correlations of our house consumption with degrees days. Rather, Marianne has investigated the relationships of the impacts of temperature, solar and wind on the heat loss of the CCHT houses. She also studied the impact of ground temperature on heat loss in the basement, which affects the energy balance of the house. Even with all of this, her correlations always showed scatter, indicating that we don’t yet have the whole story on how weather and surroundings affect heat loss. That is why we went to the trouble of building an identical Reference House to the Test House – the Reference House is like a very expensive weather sensor that integrates all these factors which we use to predict what goes on in the Test house for a given set of technologies and configuration. Even there, we have to benchmark the Reference house ‘weather sensor’ as it changes from season to season.

So the challenge you have taken on is a big one – we have no easy fixes to use HDD as a predictor or adjustment to your consumption data. Others in the business of analyzing customer consumption data in more detail, for example oil and gas utilities or consultants may have had more success.

Best regards,
Mike Swinton
Principal Research Officer
Research Manager
Canadian Centre for Housing Technology



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