High R2 of baseline model may lead to troubles

//High R2 of baseline model may lead to troubles

High R2 of baseline model may lead to troubles

When determining energy consumption baseline through regression analysis, as recommended by best practices, sensible energy managers pick a formula that relies of physics of the process, not a formula that produces the highest correlation (R2) between predicted and measured data.

In a vast majority of cases relation between energy consumed and it’s driver is linear. Here are several examples of drivers of linear consumption: production volume, degree-days, amount of heat rejected in refrigeration, volume of steam produced, volume of compressed air, volume of natural gas burned to in boiler.

Higher power polynomial always produces a higher correlation, this is how MP3 works, it does mean it better describes process. Reliance on such polynomial to assess savings may bring such ridiculous results as savings exceeding total consumption.

Baseline must work as a good predictor of future, not as the best descriptor of the past.

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