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- Regression excel explained how to#
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- Regression excel explained software#
But you should try to get energy data with at least 10 periods of measured consumption, and covering at least one full year (e.g. You might not have much choice about what energy-consumption data you use for your regression analysis.
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Regression excel explained software#
If you have detailed interval energy consumption data (typically readings taken automatically every 60 minutes or less), you can use our Energy Lens software to turn it into daily, weekly, or monthly kWh figures. one analysis for Mondays to Fridays and another for Saturdays and Sundays). Alternatively you can do separate regression analysis for occupied and unoccupied days (e.g. Unless your building operates similarly for all 7 days of the week, it's usually best to sum daily data into weekly totals and do your regression analysis on those. Unless your building is used in the same way on all 7 days of the week, these calendar mismatches will cause inaccuracies in your data analysis.ĭaily data often isn't ideal either, because of consumption differences caused by different days of the week having different occupancy/heating/cooling patterns. one month might have 5 weekends, the next might have 4). Monthly data is usually OK too, but it's rarely as good as weekly data, because the days of the week don't line up with calendar months (e.g. Although the occupancy of a building and its heating/cooling patterns might vary throughout the week, the patterns are usually fairly consistent from one week to the next. Most buildings follow a weekly routine, which means that weekly energy-consumption data is typically a good option for regression analysis. You might have detailed interval data from a smart meter, or weekly or monthly records of energy consumption that you've collected yourself, or energy bills from a utility or energy supplier. You will need records of your building's energy consumption, and degree days from a nearby weather station: Getting the energy-consumption data Our introduction to degree days should help. What to do with your regression model once you have itįirst things first, do you know what a degree day is?īefore diving into regression analysis, it helps to understand exactly what a degree day is.Handling data from multiple fuels/meters (e.g.Multiple regression for when both heating and cooling are metered together.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.
Regression excel explained how to#
how to test regressions with degree days in multiple base temperatures, to help you choose the optimal base temperature(s) for your building(s).
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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