An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to m...

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Main Authors: Abdul Razak I.A.W., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A., Baharin N., Jali H.B.
Other Authors: 56602467500
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Published: Universiti Teknikal Malaysia Melaka 2023
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spelling my.uniten.dspace-241972023-05-29T14:56:47Z An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting Abdul Razak I.A.W. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. Baharin N. Jali H.B. 56602467500 35606640500 24448864400 25824750400 8922419700 55912740900 57202805937 Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. The monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models. � 2018 Universiti Teknikal Malaysia Melaka. All Rights Reserved. Final 2023-05-29T06:56:47Z 2023-05-29T06:56:47Z 2018 Article 2-s2.0-85049394628 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049394628&partnerID=40&md5=25598ff99a690210580a23d0b5c48dc9 https://irepository.uniten.edu.my/handle/123456789/24197 10 2-May 99 103 Universiti Teknikal Malaysia Melaka Scopus
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description Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. The monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models. � 2018 Universiti Teknikal Malaysia Melaka. All Rights Reserved.
author2 56602467500
author_facet 56602467500
Abdul Razak I.A.W.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
Baharin N.
Jali H.B.
format Article
author Abdul Razak I.A.W.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
Baharin N.
Jali H.B.
spellingShingle Abdul Razak I.A.W.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
Baharin N.
Jali H.B.
An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
author_sort Abdul Razak I.A.W.
title An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
title_short An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
title_full An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
title_fullStr An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
title_full_unstemmed An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
title_sort optimization method of genetic algorithm for lssvm in medium term electricity price forecasting
publisher Universiti Teknikal Malaysia Melaka
publishDate 2023
_version_ 1806426711587291136
score 13.214268