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dc.contributor.authorSyah, Rahmad
dc.contributor.authorDavarpanah, Afshin
dc.contributor.authorElveny, Marischa
dc.contributor.authorKarmaker, Ashish Kumar
dc.contributor.authorNasution, Mahyuddin
dc.contributor.authorHossain, Md Alamgir
dc.date.accessioned2021-09-23T00:27:23Z
dc.date.available2021-09-23T00:27:23Z
dc.date.issued2021
dc.identifier.issn2079-9292
dc.identifier.doi10.3390/electronics10182214
dc.identifier.urihttp://hdl.handle.net/10072/408239
dc.description.abstractThis paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom2214
dc.relation.ispartofissue18
dc.relation.ispartofjournalElectronics
dc.relation.ispartofvolume10
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchApplied economics
dc.subject.fieldofresearchEconomic models and forecasting
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode3801
dc.subject.fieldofresearchcode380203
dc.titleForecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSyah, R; Davarpanah, A; Elveny, M; Karmaker, AK; Nasution, M; Hossain, MA, Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm, Electronics, 10 (18), pp. 2214
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-09-22T23:50:58Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorHossain, Md. Alamgir


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