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

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dc.contributor.authorKhan, Zulfiqar Ahmad
dc.contributor.authorHussain, Tanveer
dc.contributor.authorUllah, Amin
dc.contributor.authorUllah, Waseem
dc.contributor.authorDel Ser, Javier
dc.contributor.authorMuhammad, Khan
dc.contributor.authorSajjad, Muhammad
dc.contributor.authorBaik, Sung Wook
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T13:25:02Z
dc.date.available2024-09-10T13:25:02Z
dc.date.issued2023-09-01
dc.descriptionPublisher Copyright: © 2023 Elsevier B.V.
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019M3F2A1073179).
dc.description.statusPeer reviewed
dc.identifier.citationKhan , Z A , Hussain , T , Ullah , A , Ullah , W , Del Ser , J , Muhammad , K , Sajjad , M & Baik , S W 2023 , ' Modelling Electricity Consumption During the COVID19 Pandemic : Datasets, Models, Results and a Research Agenda ' , Energy and Buildings , vol. 294 , 113204 . https://doi.org/10.1016/j.enbuild.2023.113204
dc.identifier.doi10.1016/j.enbuild.2023.113204
dc.identifier.issn0378-7788
dc.identifier.urihttps://hdl.handle.net/11556/5082
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85162212286&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofEnergy and Buildings
dc.relation.projectIDMinistry of Science, ICT and Future Planning, MSIP, 2019M3F2A1073179
dc.relation.projectIDNational Research Foundation of Korea, NRF
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAnalysis
dc.subject.keywordsAttention GRU
dc.subject.keywordsCOVID19
dc.subject.keywordsDeep learning
dc.subject.keywordsElectricity consumption
dc.subject.keywordsMachine learning
dc.subject.keywordsPost-pandemic consumption
dc.subject.keywordsPre-pandemic consumption
dc.subject.keywordsCivil and Structural Engineering
dc.subject.keywordsBuilding and Construction
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsElectrical and Electronic Engineering
dc.titleModelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agendaen
dc.typejournal article
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