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A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level

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  • García, Sebastián
  • Parejo, Antonio
  • Personal, Enrique
  • Ignacio Guerrero, Juan
  • Biscarri, Félix
  • León, Carlos

Abstract

Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis.

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  • García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:appene:v:287:y:2021:i:c:s0306261921000969
    DOI: 10.1016/j.apenergy.2021.116547
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    11. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
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    17. Costa, Vinicius B.F. & Pereira, Lígia C. & Andrade, Jorge V.B. & Bonatto, Benedito D., 2022. "Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model," Applied Energy, Elsevier, vol. 313(C).
    18. Georgeta Soava & Anca Mehedintu & Mihaela Sterpu & Eugenia Grecu, 2021. "The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania," Energies, MDPI, vol. 14(9), pages 1-25, April.
    19. Diana-Mihaela Jula, 2021. "Impact of the COVID-19 Pandemic on the Romanian Electricity Consumption," Global Economic Observer, "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences;Institute for World Economy of the Romanian Academy, vol. 9(1), pages 166-179, June.
    20. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    21. Ankitha Nandipura Prasanna & Priscila Grecov & Angela Dieyu Weng & Christoph Bergmeir, 2022. "Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand," Papers 2209.08885, arXiv.org, revised Oct 2022.
    22. Biljana Kulisic & Bruno Gagnon & Jörg Schweinle & Sam Van Holsbeeck & Mark Brown & Jurica Simurina & Ioannis Dimitriou & Heather McDonald, 2021. "The Contributions of Biomass Supply for Bioenergy in the Post-COVID-19 Recovery," Energies, MDPI, vol. 14(24), pages 1-31, December.
    23. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).

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