  Forecasting thermal load is a key component for the majority of optimization
solutions for controlling district heating and cooling systems. Recent studies
have analysed the results of a number of data-driven methods applied to thermal
load forecasting, this paper presents the results of combining a collection of
these individual methods in an expert system. The expert system will combine
multiple thermal load forecasts in a way that it always tracks the best expert
in the system. This solution is tested and validated using a thermal load
dataset of 27 months obtained from 10 residential buildings located in Rottne,
Sweden together with outdoor temperature information received from a weather
forecast service. The expert system is composed of the following data-driven
methods: linear regression, extremely randomized trees regression, feed-forward
neural network and support vector machine. The results of the proposed solution
are compared with the results of the individual methods.
