![]() The best method of all – weighted day normalization.Improved method for irregular data (with unweighted day normalization).Simple regression of energy usage against degree days (with no day normalization).Regression is key to most effective analysis of heating/cooling energy consumption, so it is important to understand it well. Even if you never actually use them in Excel, understanding them will help you understand how our regression tool works and how best to take advantage of it. Just visit the Degree web tool, select "Regression" as the data type, and follow the instructions from there.īut we do think it's worthwhile to understand the basic theory and Excel-based processes that we explain here. ![]() It does everything described in this article and more, and it gives better results than Excel too (for reasons we explain in this article). If you want to get straight to analyzing your energy data, you could just use the regression tool on our website. how to test regressions with degree days in multiple base temperatures, to help you choose the optimal base temperature(s) for your building(s).how to do regression analysis of energy-consumption data and degree days in Excel.So we have written this article to explain only what is relevant for energy-data analysis, specifically: There are many text books and online resources that explain regression analysis in detail, but the theory can get a little heavy going. We get a lot of questions along the lines of "how do I do this using degree days?" It's very common for the answers to involve regression analysis. Regression Analysis of Energy Consumption and Degree Days in Excel
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