Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms

It is shown that the results are in line with expectations based on prior work, and that the demand predictions have a robust behaviour within acceptable error margins. Applications of such predictions in relation to intelligent network controllers for district heating are explored and the initial results of such systems are discussed.