We'll call this the " before data" and the " after data".Įither the before data, or the after data, or both, will need to be broken down into dated periods (e.g. You'll need energy-usage data from before the changes/improvements you've made, and from after too. We'll explain the process step by step: Step 1: Assemble your energy data You might hear it loosely called " weather correction", or " weather normalization", but these terms can imply a variety of calculations, so we need to be more precise. It involves degree days and regression analysis. To calculate or prove the energy savings you've made, you'll need to correct for the weather variations somehow.įortunately there is a well-established process for calculating energy savings in situations like these. You might have energy-usage data from before and after your improvements, but you can't just compare the before-and-after figures like-for-like if the period before your improvements was hotter or colder than the period after your improvements.
Colder weather means more energy for heating, and warmer weather means more energy for cooling.
At some point you'll probably want to work out how much energy you've saved.īut heating and cooling energy consumption varies with the weather, and this complicates things. Maybe you've installed new insulation, or upgraded your HVAC system, or campaigned to encourage your coworkers to switch things off, or made some other improvements to increase efficiency. So you've been working to reduce your energy consumption. How to Calculate or Prove Energy Savings Using Degree Days and Regression
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