Heating Demand Forecasting with Multiple Regression: Model Setup and Case Study

Abstract

Accurate demand forecasting in district heating networks is an essential and imperative task in the everyday operation of both, the network itself and the heating energy suppliers. Multiple regression is one of the possible approaches to solving the forecasting problem with sufficient accuracy and little computational effort. This paper presents a polynomial regression model and offers several additions for its further improvement. It is found that grouping the model residuals by hour-of-day allows notably reducing the forecast error. The value of other modifications and the optimum size of the training set can vary over time, thus an automatic model parameter selection before each new forecast is advised.