Near real-time load forecasting of power system using fuzzy time series, artificial neural networks, and wavelet transform models

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dc.authorid0000-0002-2961-0035en_US
dc.authorid0000-0002-1821-0722en_US
dc.contributor.authorKhatoon, Shahida
dc.contributor.authorIbraheem, Mohammad
dc.contributor.authorShahid, Mohammad
dc.contributor.authorÇelik, Emre
dc.contributor.authorBekiroğlu, Erdal
dc.contributor.authorSharma, Gulshan
dc.date.accessioned2024-05-24T08:55:25Z
dc.date.available2024-05-24T08:55:25Z
dc.date.issued2024en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDue to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand.en_US
dc.identifier.citationKhatoon, S., Ibraheem, Shahid, M., Sharma, G., Çelik, E., Bekiroğlu, E., ... & Priti. (2024). Near Real-Time Load Forecasting of Power System Using Fuzzy Time Series, Artificial Neural Networks, and Wavelet Transform Models. Electric Power Components and Systems, 52(5), 796-810.en_US
dc.identifier.doi10.1080/15325008.2023.2235586
dc.identifier.endpage810en_US
dc.identifier.issn1532-5008
dc.identifier.issn1532-5016
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85169335853en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage796en_US
dc.identifier.urihttp://dx.doi.org/10.1080/15325008.2023.2235586
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12158
dc.identifier.volume52en_US
dc.identifier.wosWOS:001062457200001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBekiroğlu, Erdal
dc.language.isoenen_US
dc.publisherTaylor & Francis Inc.en_US
dc.relation.ispartofElectric Power Components and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectAutomatic Generation Control (AGC)en_US
dc.subjectFuzzy Time Series (FTS)en_US
dc.subjectLoad Forecasten_US
dc.subjectPower System Operationen_US
dc.subjectWavelet Transformen_US
dc.titleNear real-time load forecasting of power system using fuzzy time series, artificial neural networks, and wavelet transform modelsen_US
dc.typeArticleen_US

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