Forecasting District Heating Consumption Based on Customer Measurements

摘要

The target of the present study was to develop a forecasting model for district heating (DH) consumption using hourly heat consumption data from individual customers. The starting point of this research was that more accurate heat consumption data is now available directly from the customers and almost in real time. Earlier forecasting models were based on overall production data for entire heating networks or sections. The forecasting model was constructed based on linear regression where outdoor temperature predicts heat consumption. A weekly rhythm of the heat consumption was added to the model to increase the accuracy. More accurate DH forecasting models may benefit both energy companies and customers and a similar approach suits also for forecasting cooling demand.