Analysis of SARIMA Models for Forecasting Electricity Demand

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dc.contributor.authorAksoz, Ahmet
dc.contributor.authorOyucu, Saadin
dc.contributor.authorBicer, Emre
dc.contributor.authorBayindir, Ramazan
dc.date.accessioned2024-10-26T17:59:52Z
dc.date.available2024-10-26T17:59:52Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description12th International Conference on Smart Grid (ICSmartGrid) -- MAY 27-29, 2024 -- Setubal, PORTUGAL
dc.description.abstractThis article presents an in-depth evaluation of electricity consumption predictions using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Leveraging historical electricity consumption data, the SARIMA model demonstrates a commendable ability to forecast future consumption patterns. Our analysis reveals a strong alignment between the model's predictions and actual consumption data, affirming the efficacy of time series modeling in capturing complex energy consumption dynamics. Notably, while the model excels in predicting near-term consumption trends, uncertainties widen for long-term forecasts, prompting critical reflections on the model's evolving accuracy and reliability over time. For future research endeavors, we recommend comparing the performance of diverse time series models to discern optimal modeling approaches. Further optimization of model parameters stands as a paramount endeavor to refine prediction accuracy and mitigate uncertainties. Specifically, efforts to identify and address potential overfitting or underfitting tendencies within the model are advised. Additionally, leveraging supplementary data sources and integrating seasonal factors could bolster the reliability of future predictions, expanding the model's predictive scope and ensuring more robust and precise forecasts.
dc.description.sponsorshipIEEE,IEEE Ind Applicat Soc,IEEE IES,Int Journal Renewable Energy Res,IjSmartGrid,Toshiba Mitsubishi Elect Ind Syst Corp
dc.description.sponsorshipEuropean Union's Horizon Europe research and innovation program [101084323]
dc.description.sponsorshipThis research was supported by the European Union's Horizon Europe research and innovation program under grant agreement No. 101084323, project BLOW (Black Sea fLoating Offshore Wind).
dc.identifier.doi10.1109/icSmartGrid61824.2024.10578181
dc.identifier.endpage771
dc.identifier.isbn979-8-3503-6161-2
dc.identifier.isbn979-8-3503-6162-9
dc.identifier.scopus2-s2.0-85199426032
dc.identifier.scopusqualityN/A
dc.identifier.startpage767
dc.identifier.urihttps://doi.org/10.1109/icSmartGrid61824.2024.10578181
dc.identifier.urihttps://hdl.handle.net/20.500.12418/27373
dc.identifier.wosWOS:001266130300126
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof12th International Conference on Smart Grid, Icsmartgrid 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjecttime series analysis
dc.subjectsarima model
dc.subjectenergy consumption forecasting
dc.titleAnalysis of SARIMA Models for Forecasting Electricity Demand
dc.typeConference Object

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