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Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic

Accurate 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 accura...

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Detalles Bibliográficos
Autores principales: Chaianong, Aksornchan, Winzer, Christian, Gellrich, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283696/
http://dx.doi.org/10.1016/j.esr.2022.100895
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author Chaianong, Aksornchan
Winzer, Christian
Gellrich, Mario
author_facet Chaianong, Aksornchan
Winzer, Christian
Gellrich, Mario
author_sort Chaianong, Aksornchan
collection PubMed
description Accurate 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.
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spelling pubmed-92836962022-07-15 Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic Chaianong, Aksornchan Winzer, Christian Gellrich, Mario Energy Strategy Reviews Article Accurate 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. The Authors. Published by Elsevier Ltd. 2022-09 2022-07-15 /pmc/articles/PMC9283696/ http://dx.doi.org/10.1016/j.esr.2022.100895 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Chaianong, Aksornchan
Winzer, Christian
Gellrich, Mario
Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title_full Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title_fullStr Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title_full_unstemmed Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title_short Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic
title_sort impacts of traffic data on short-term residential load forecasting before and during the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283696/
http://dx.doi.org/10.1016/j.esr.2022.100895
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