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Forecasting thermal load is a key component for the majority of optimizationsolutions for controlling district heating and cooling systems. Recent studieshave analysed the results of a number of data-driven methods applied to thermalload forecasting, this paper presents the results of combining a collection ofthese individual methods in an expert system. The expert system will combinemultiple thermal load forecasts in a way that it always tracks the best expertin the system. This solution is tested and validated using a thermal loaddataset of 27 months obtained from 10 residential buildings located in Rottne,Sweden together with outdoor temperature information received from a weatherforecast service. The expert system is composed of the following data-drivenmethods: linear regression, extremely randomized trees regression, feed-forwardneural network and support vector machine. The results of the proposed solutionare compared with the results of the individual methods.

Operational thermal load forecasting in district heating networks using machine learning and expert advice