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Spatio-temporal modelling of the effects of the COVID-19 pandemic on electricity consumption patterns in Stockholm, Sweden
Swedish Defence University, Department of Systems Science for Defence and Security, Systems Science for Defence and Security Division. Uppsala University, Department of Information Technology, Uppsala, Sweden.ORCID iD: 0000-0002-3017-0874
Uppsala University, Department of Information Technology, Uppsala, Sweden, (SWE).
Uppsala University, Department of Civil and Industrial Engineering, Uppsala, Sweden, (SWE).
Uppsala University, Department of Civil and Industrial Engineering, Uppsala, Sweden (SWE).
2024 (English)In: Science Talks, ISSN 2772-5693, Vol. 9, article id 100300Article in journal (Refereed) Published
Abstract [en]

The COVID-19 pandemic has had drastic effects on the way we live. With the sudden shift to the home office, a shift in electricity consumption has also taken place between the residential, public, industrial and commercial sectors. Understanding these changes is crucial for a sustainable energy transition, as these changes affect the load and flexibility to adapt. In this study, we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns. Based on data from around 10,000 smart electricity meters in Stockholm, Sweden, we built a spatio-temporal multivariate regression model at postal code level to predict what the total electricity consumption during the pandemic would have looked like if there were no pandemic, allowing for quantification of the effect of the pandemic as the difference between the predicted and actual consumption, adjusted for differences in temperature. Results of 10-fold cross-validation showed good accuracy with a Mean Absolute Percentage Error (MAPE) of around 5%, with slight variations for season and consumer sectors. The prediction maps allow us to find clusters where electricity consumption changes are highest, showing where changes in the local electricity infrastructure may be needed in the future.

Place, publisher, year, edition, pages
2024. Vol. 9, article id 100300
Keywords [en]
Electricity consumption, COVID-19, Smart meter data, Regression model, Demand response, Spatio-temporal
National Category
Energy Systems
Research subject
Systems science for defence and security
Identifiers
URN: urn:nbn:se:fhs:diva-12189DOI: 10.1016/j.sctalk.2024.100300OAI: oai:DiVA.org:fhs-12189DiVA, id: diva2:1834121
Funder
Swedish Energy Agency, P2021-00187Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-03-11Bibliographically approved

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van Zoest, Vera

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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