Adaptive Weather Correction of Energy Consumption Data

Abstract

A framework for adaptive weather correction of energy consumption data is presented. The procedure is conducted in two steps: I) a regression model is trained on a building's recent historical energy consumption, weather and calendar data; II) energy consumption is predicted by using long term weather data as input to the trained model. Thus the buildings long term energy consumption is obtained, from which the building's expected (alias normalised or weather corrected) yearly energy consumption is derived. For older Swedish residential buildings, the proposed regression method matches traditional heating degree days method in accuracy. But for low energy and near zero energy buildings the regression method is more accurate, especially for years of extreme weather and for building with more complex installations such as heat pumps or solar thermal panels. The main benefit of the developed weather correction method is that it adapts to the data, therefore most buildings (or any kinds of weather dependent processes) can be weather corrected in an automated way.