Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period

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dc.authorscopusid58484114400
dc.authorscopusid56716527100
dc.authorscopusid37071971700
dc.authorscopusid57223961736
dc.authorscopusid55062117500
dc.contributor.authorAl-azzawi, Saif Mohammed
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorAttar, Hani
dc.contributor.authorAmer, Ayman
dc.contributor.authorSolyman, Ahmed A. A.
dc.date.accessioned2024-09-11T19:58:14Z
dc.date.available2024-09-11T19:58:14Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractDue to global lockdown policies implemented against COVID-19, there has been an impact on electricity consumption. Several countries have emphasized the significance of ensuring electricity supply security during the pandemic to maintain the livelihood of people. Accurate forecasting of electricity demand plays a crucial role in ensuring energy security across all nations; accordingly to achieve this objective, this study employs metaheuristics optimization algorithms to enhance the prediction model's operation, such as Support Vector Machine (SVM), KNearest Neighbors (KNN), and Random Forest (RF), at an optimized level to minimize errors. Two metaheuristics optimization methods, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are utilized. The suggested prediction models are trained using daily power usage data from three US urban regions. In terms of prediction accuracy, the findings show that KNN with PSO surpasses the other models. The COVID-19 pandemic reduced power usage by 20% relative to pre-pandemic levels. © 2023, International Journal of Intelligent Engineering and Systems. All Rights Reserved.en_US
dc.identifier.doi10.22266/ijies2023.0831.20
dc.identifier.endpage253en_US
dc.identifier.issn2185-310Xen_US
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85164570965en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage239en_US
dc.identifier.urihttps://doi.org/10.22266/ijies2023.0831.20
dc.identifier.urihttps://hdl.handle.net/11363/8449
dc.identifier.volume16en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIntelligent Network and Systems Societyen_US
dc.relation.ispartofInternational Journal of Intelligent Engineering and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectCOVID-19; Genetic algorithms; Metaheuristics optimization algorithms; Particle swarm optimization; Support vector machineen_US
dc.titleHyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Perioden_US
dc.typeArticleen_US

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