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Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems)ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0002-9473-4536
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-4887-9547
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 218, p. 159-172Article in journal (Refereed) Published
Abstract [en]

Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.

Place, publisher, year, edition, pages
2018. Vol. 218, p. 159-172
Keywords [en]
Gaussian process, Probabilistic load forecasting, Residential load
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
URN: urn:nbn:se:uu:diva-345282DOI: 10.1016/j.apenergy.2018.02.165OAI: oai:DiVA.org:uu-345282DiVA, id: diva2:1188936
Funder
Swedish Energy AgencyAvailable from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-03-09Bibliographically approved

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Shepero, Mahmoudvan der Meer, DennisMunkhammar, JoakimWidén, Joakim

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