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Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-5601-1687
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
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2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 213, p. 195-207Article in journal (Refereed) Published
Abstract [en]

This paper presents a study into the utilization of Gaussian Processes (GPs) for probabilistic forecasting of residential electricity consumption, photovoltaic (PV) power generation and net demand of a single household. The covariance function that encodes prior belief on the general shape of the time series plays a vital role in the performance of GPs and a common choice is the squared exponential (SE), although it has been argued that the SE is likely suboptimal for physical processes. Therefore, we thoroughly test various (combinations of) covariance functions. Furthermore, in order bypass the substantial learning and inference time accompanied with GPs, we investigate the potential of dynamically updating the hyperparameters using a moving training window and assess the consequences on predictive accuracy. We show that the dynamic GP produces sharper prediction intervals (PIs) than the static GP with significant lower computational burden, but at the cost of the ability to capture sharp peaks. In addition, we examine the difference in accuracy between a direct and indirect forecasting strategy in case of net demand forecasting and show that the latter is prone to producing wider PIs with higher coverage probability.

Place, publisher, year, edition, pages
2018. Vol. 213, p. 195-207
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-340597DOI: 10.1016/j.apenergy.2017.12.104ISI: 000425576900017OAI: oai:DiVA.org:uu-340597DiVA, id: diva2:1179402
Funder
Swedish Energy AgencyAvailable from: 2018-01-28 Created: 2018-02-01 Last updated: 2018-04-25Bibliographically approved

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van der Meer, Dennis W.Shepero, MahmoudSvensson, AndreasWidén, JoakimMunkhammar, Joakim

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