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NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM

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dc.contributor.authorRazak I.A.W.A.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorSiah Y.K.en_US
dc.contributor.authorSulaima M.F.en_US
dc.contributor.authorid57913626600en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid57913418100en_US
dc.contributor.authorid56024233000en_US
dc.date.accessioned2023-05-29T09:36:31Z
dc.date.available2023-05-29T09:36:31Z
dc.date.issued2022
dc.description.abstractPredicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts. � 2022 Penerbit UTM Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11113/AEJ.V12.17276
dc.identifier.epage17
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85139153729
dc.identifier.spage11
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139153729&doi=10.11113%2fAEJ.V12.17276&partnerID=40&md5=154dc79fd253ff77f8cbddedc8c6f0af
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26752
dc.identifier.volume12
dc.publisherPenerbit UTM Pressen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleASEAN Engineering Journal
dc.titleNEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHMen_US
dc.typeArticleen_US
dspace.entity.typePublication
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