Determination of influential parameters for heat consumption in district heating systems using machine learning

District heating systems are an important part of the future smart energy systems and are seen in the European Union as a vehicle for reaching energy efficiency targets. Integrating different energy systems requires high prediction accuracy for all energy sub-systems. Within this paper a data analysis was made with the goal of identifying a high accuracy prediction model and ranking the most influential parameters on heat consumption of final consumers in district heating systems. The data set consisted of the actual billing data comprising of 260 buildings and it was additionally supplemented by the behavioural data obtained from interviews and questionnaires conducted on the demonstration building in Zagreb, Croatia. The authors choose regression trees, random forest and regression support vector machines as algorithms for testing prediction accuracy and evaluating the variable importance ranking on the data set. The best performing algorithm was random forest, resulting with high prediction accuracy and the root mean squared error of prediction of specific annual heat consumption below 1 kWh/m2. Furthermore, all analysed machine learning algorithms ranked importance variables for both technical and behavioural parameters, giving the indication what parameters should be influenced in order to reach specific targets, such as energy savings.