Heating and cooling degree days look simple: how far the day sits above or below a comfort baseline. But HDD and CDD quietly drive a huge amount of real money, because they stand in for energy demand. A small miss in the degree-day forecast becomes a real miss in the load, gas, or power forecast built on top of it.
Where the error creeps in
Degree-day forecasts inherit whatever the underlying temperature forecast gets wrong, and temperature forecasts drift. A model can be biased a degree or two warm in a particular setup, which does not sound like much until you multiply it across a region and a season. The bias is rarely random. It tends to show up in specific patterns the model has not learned to correct.
Why it matters more than it looks
Gas and power demand track degree days closely, so a degree-day forecast that runs consistently warm leads to demand you under-count and bids you misprice. Retailers planning seasonal inventory feel it too. The forecast does not have to be wildly off to be expensive. It just has to be quietly, consistently biased.
Correcting the bias, continuously
This is a textbook case for a layer on top. Rather than rebuilding your temperature or degree-day model, you add something that watches its forecasts against what actually verified, spots the patterns where it runs warm or cold, and corrects for them going forward. As the season shifts and the biases change, the correction keeps up.
A degree or two is not noise. In degree-day land, a small, repeated bias is the difference between a forecast you can trade on and one you cannot.
Weather is where we first proved the core, improving every major model we tested by roughly 40% on average. Degree days are weather distilled into the number that moves energy demand, which makes them one of the highest-value places to keep a forecast honest.
Trading or planning on degree days?
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