District Heating Demand Short-Term Forecasting

This paper discusses various forecasting tools that can be used in predicting the thermal load in district heating networks, focusing on day-ahead hourly planning as it is particularly important for cogeneration plants participating in electricity wholesale markets. Forecasts obtained by employing an artificial neural network are compared to a polynomial regression model. Their ability to supplement each other in a combined forecasting tool has been considered as well. Prediction inaccuracy cost is observed and suggested as evaluation criterion. The case studies are based on the district heating network in Riga, Latvia. Recorded data sets of temperature and heat demand are applied for thermal load prediction.