Knowledge Hub search
Operational thermal load forecasting in district heating networks using machine learning and expert advice
Recent studies have analysed the results of a number of data-driven methods applied to thermal load forecasting, this paper (2018) 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.
Author: Horizon 2020 STORM project
Keywords: optimisation, district heating, district cooling, thermal load forecast, data, digitalisation, 4th generation district heating, energy efficiency, Horizon 2020, buildings, model, prediction algorithms
Type of Content
- Scientific article