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Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid...

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Autores principales: Khan, Zulfiqar Ahmad, Hussain, Tanveer, Ullah, Amin, Ullah, Waseem, Del Ser, Javier, Muhammad, Khan, Sajjad, Muhammad, Baik, Sung Wook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226901/
https://www.ncbi.nlm.nih.gov/pubmed/37342253
http://dx.doi.org/10.1016/j.enbuild.2023.113204
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author Khan, Zulfiqar Ahmad
Hussain, Tanveer
Ullah, Amin
Ullah, Waseem
Del Ser, Javier
Muhammad, Khan
Sajjad, Muhammad
Baik, Sung Wook
author_facet Khan, Zulfiqar Ahmad
Hussain, Tanveer
Ullah, Amin
Ullah, Waseem
Del Ser, Javier
Muhammad, Khan
Sajjad, Muhammad
Baik, Sung Wook
author_sort Khan, Zulfiqar Ahmad
collection PubMed
description The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.
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spelling pubmed-102269012023-05-30 Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda Khan, Zulfiqar Ahmad Hussain, Tanveer Ullah, Amin Ullah, Waseem Del Ser, Javier Muhammad, Khan Sajjad, Muhammad Baik, Sung Wook Energy Build Article The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns. Elsevier B.V. 2023-09-01 2023-05-30 /pmc/articles/PMC10226901/ /pubmed/37342253 http://dx.doi.org/10.1016/j.enbuild.2023.113204 Text en © 2023 Elsevier B.V. All rights reserved. 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
Khan, Zulfiqar Ahmad
Hussain, Tanveer
Ullah, Amin
Ullah, Waseem
Del Ser, Javier
Muhammad, Khan
Sajjad, Muhammad
Baik, Sung Wook
Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title_full Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title_fullStr Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title_full_unstemmed Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title_short Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda
title_sort modelling electricity consumption during the covid19 pandemic: datasets, models, results and a research agenda
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226901/
https://www.ncbi.nlm.nih.gov/pubmed/37342253
http://dx.doi.org/10.1016/j.enbuild.2023.113204
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