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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25718
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dc.contributor.authorChaianong, Aksornchan-
dc.contributor.authorWinzer, Christian-
dc.contributor.authorGellrich, Mario-
dc.date.accessioned2022-09-30T14:14:26Z-
dc.date.available2022-09-30T14:14:26Z-
dc.date.issued2022-
dc.identifier.issn2211-467Xde_CH
dc.identifier.issn2211-4688de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25718-
dc.description.abstractAccurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofEnergy Strategy Reviewsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectCOVID-19de_CH
dc.subjectLoad forecastingde_CH
dc.subjectMachine learningde_CH
dc.subjectRandom forestde_CH
dc.subjectTrafficde_CH
dc.subject.ddc333.79: Energiede_CH
dc.titleImpacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemicde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Wirtschaftsinformatik (IWI)de_CH
zhaw.organisationalunitZentrum für Energie und Umwelt (CEE)de_CH
dc.identifier.doi10.1016/j.esr.2022.100895de_CH
dc.identifier.doi10.21256/zhaw-25718-
zhaw.funding.euNode_CH
zhaw.issue43de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start100895de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2022de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf196304de_CH
zhaw.webfeedW: Spitzenpublikationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Chaianong, A., Winzer, C., & Gellrich, M. (2022). Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic. Energy Strategy Reviews, 2022(43), 100895. https://doi.org/10.1016/j.esr.2022.100895
Chaianong, A., Winzer, C. and Gellrich, M. (2022) ‘Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic’, Energy Strategy Reviews, 2022(43), p. 100895. Available at: https://doi.org/10.1016/j.esr.2022.100895.
A. Chaianong, C. Winzer, and M. Gellrich, “Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic,” Energy Strategy Reviews, vol. 2022, no. 43, p. 100895, 2022, doi: 10.1016/j.esr.2022.100895.
CHAIANONG, Aksornchan, Christian WINZER und Mario GELLRICH, 2022. Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic. Energy Strategy Reviews. 2022. Bd. 2022, Nr. 43, S. 100895. DOI 10.1016/j.esr.2022.100895
Chaianong, Aksornchan, Christian Winzer, and Mario Gellrich. 2022. “Impacts of Traffic Data on Short-Term Residential Load Forecasting before and during the COVID-19 Pandemic.” Energy Strategy Reviews 2022 (43): 100895. https://doi.org/10.1016/j.esr.2022.100895.
Chaianong, Aksornchan, et al. “Impacts of Traffic Data on Short-Term Residential Load Forecasting before and during the COVID-19 Pandemic.” Energy Strategy Reviews, vol. 2022, no. 43, 2022, p. 100895, https://doi.org/10.1016/j.esr.2022.100895.


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