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Short term electricity price forecasting with multistage optimization technique of LSSVM-GA

Price prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful fo...

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Main Authors: Razak I.A.W.A., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A.
Other Authors: 56602467500
Format: Article
Published: Universiti Teknikal Malaysia Melaka 2023
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spelling my.uniten.dspace-233972023-05-29T14:40:08Z Short term electricity price forecasting with multistage optimization technique of LSSVM-GA Razak I.A.W.A. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. 56602467500 35606640500 24448864400 25824750400 8922419700 Price prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by three stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models. Final 2023-05-29T06:40:08Z 2023-05-29T06:40:08Z 2017 Article 2-s2.0-85032900285 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032900285&partnerID=40&md5=00432bb9d01260e295184d5f0038d89c https://irepository.uniten.edu.my/handle/123456789/23397 9 2-Jul 117 122 Universiti Teknikal Malaysia Melaka Scopus
institution Universiti Tenaga Nasional
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country Malaysia
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description Price prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by three stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.
author2 56602467500
author_facet 56602467500
Razak I.A.W.A.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
format Article
author Razak I.A.W.A.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
spellingShingle Razak I.A.W.A.
Abidin I.Z.
Siah Y.K.
Abidin A.A.Z.
Rahman T.K.A.
Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
author_sort Razak I.A.W.A.
title Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
title_short Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
title_full Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
title_fullStr Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
title_full_unstemmed Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
title_sort short term electricity price forecasting with multistage optimization technique of lssvm-ga
publisher Universiti Teknikal Malaysia Melaka
publishDate 2023
_version_ 1806425657782042624
score 13.222552

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