A latent variable approach to heat load prediction in thermal grids

Document identifier: oai:DiVA.org:ltu-77819
Access full text here:10.23919/ECC51009.2020.9143860
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Control Engineering, Teknik och teknologier, Elektroteknik och elektronik, Reglerteknik
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 7 Affordable and clean energy
The SDG label(s) above have been assigned by OSDG.ai


In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises mainly from time dependent operation of space heating and ventilation, and domestic hot water production. The resulting model is recursively updated on the basis of a hyper-parameter free implementation that results in a parsimonious model allowing for high computational performance. The approach is applied to a single multi-dwelling building in Luleå, Sweden, predicting the heat load using a relatively small number of model parameters and easily obtained measurements. The results are compared with predictions using an artificial neural network, showing that the proposed method achieves better prediction accuracy for the validation case. Additionally, the proposed methods exhibits explainable behavior through the use of an interpretable physical model.


Johan Simonsson

Luleå tekniska universitet; Signaler och system; Optimation AB, Uppsala, Sweden
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Khalid Atta

Luleå tekniska universitet; Signaler och system
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Dave Zachariah

Department of Information Technology, Uppsala University, Sweden
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Wolfgang Birk

Luleå tekniska universitet; Signaler och system
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