Publication:
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting

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dc.citedby1
dc.contributor.authorIntan Azmira W.A.R.en_US
dc.contributor.authorIzham Z.A.en_US
dc.contributor.authorKeem Siah Y.en_US
dc.contributor.authorTitik Khawa A.R.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid57035448200en_US
dc.date.accessioned2023-05-29T06:01:08Z
dc.date.available2023-05-29T06:01:08Z
dc.date.issued2015
dc.description.abstractForecasting price has now become essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increasing the utility's profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide.en_US
dc.description.natureFinalen_US
dc.identifier.epage166
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84952937786
dc.identifier.spage159
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84952937786&partnerID=40&md5=b4635e6cf680a9bb0369e214674404f9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22463
dc.identifier.volume15
dc.publisherUniversitatea Politehnica din Timisoaraen_US
dc.sourceScopus
dc.sourcetitleJournal of Electrical Engineering
dc.titleFeature selection and parameter optimization with GA-LSSVM in electricity price forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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