Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic

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dc.authoridZor, Kas�m/0000-0001-6443-114X
dc.authoridCebeci, Cagatay/0000-0003-2644-1261
dc.contributor.authorCebeci, Cagatay
dc.contributor.authorZor, Kasim
dc.date.accessioned2026-02-27T07:33:01Z
dc.date.available2026-02-27T07:33:01Z
dc.date.issued2025
dc.description.abstractThe power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by 8.27% and 14.32%, respectively. For the computational times, the DNN appears to be much faster than the GEP by 82.83% and 78.56% in the hour-ahead and day-ahead forecasting, respectively.
dc.description.sponsorshipScientific Project Unit of Adana Alparslan Turkescedil; Science and Technology University [24103005]
dc.description.sponsorshipThis research was funded by Scientific Project Unit of Adana Alparslan Turke & scedil; Science and Technology University grant number 24103005.
dc.identifier.doi10.3390/app15052843
dc.identifier.issn2076-3417
dc.identifier.issue5
dc.identifier.urihttp://dx.doi.org/10.3390/app15052843
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4418
dc.identifier.volume15
dc.identifier.wosWOS:001442369300001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectelectricity
dc.subjectdemand
dc.subjectforecasting
dc.subjectDeep Polynomial Neural Networks (DNN)
dc.subjectGene Expression Programming (GEP)
dc.subjectCOVID-19 pandemic
dc.titleElectricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic
dc.typeArticle

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