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Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms
This paper (2017) presents the current status and results from extensive work in the development, implementation and operational service of online machine learning algorithms for demand forecasting. Recent results and experiences are compared to results predicted by previous work done by the authors. The prior work, based mainly on certain decision tree based regression algorithms, is expanded to include other forms of decision tree solutions as well as neural network based approaches. These algorithms are analysed both individually and combined in an ensemble solution. Furthermore, the paper also describes the practical implementation and commissioning of the system in two different operational settings where the data streams are analysed online in real-time.
Author: Horizon 2020 STORM project
Keywords: district heating, metering, self-learning control, control algorithm, 4th generation district heating, optimisation, energy efficiency, heat loat forecast, smart system, smart grid, digitalisation, energy research
Type of Content
- Scientific article