An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm
Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified b...
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2023
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my.uniten.dspace-238312023-05-29T14:52:13Z An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm Razak I.A.W.A. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. Baharin N. Jali M.H.B. 56602467500 35606640500 24448864400 25824750400 8922419700 55912740900 56078350800 Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most 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 hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models. � 2018 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T06:52:13Z 2023-05-29T06:52:13Z 2018 Article 10.11591/ijeecs.v10.i2.pp748-755 2-s2.0-85042798521 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042798521&doi=10.11591%2fijeecs.v10.i2.pp748-755&partnerID=40&md5=de06a1fd68c4495a9878adf9dcaf9ce9 https://irepository.uniten.edu.my/handle/123456789/23831 10 2 748 755 All Open Access, Hybrid Gold, Green Institute of Advanced Engineering and Science Scopus |
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Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most 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 hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models. � 2018 Institute of Advanced Engineering and Science. All rights reserved. |
| author2 |
56602467500 |
| author_facet |
56602467500 Razak I.A.W.A. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. Baharin N. Jali M.H.B. |
| format |
Article |
| author |
Razak I.A.W.A. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. Baharin N. Jali M.H.B. |
| spellingShingle |
Razak I.A.W.A. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. Baharin N. Jali M.H.B. An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| author_sort |
Razak I.A.W.A. |
| title |
An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| title_short |
An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| title_full |
An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| title_fullStr |
An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| title_full_unstemmed |
An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| title_sort |
hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2023 |
| _version_ |
1806427523511222272 |
| score |
13.222552 |