A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for 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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my.uniten.dspace-249062023-05-29T15:28:37Z A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting Razak I.A.W.A. Ibrahim N.N.A.N. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. 56602467500 57208926032 35606640500 24448864400 25824750400 8922419700 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 exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. 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-BFOA shows higher forecast accuracy with lower complexity than the existing models. � Universiti Tun Hussein Onn Malaysia Publisher's Office. Final 2023-05-29T07:28:36Z 2023-05-29T07:28:36Z 2019 Article 10.30880/ijie.2019.11.03.024 2-s2.0-85075977485 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075977485&doi=10.30880%2fijie.2019.11.03.024&partnerID=40&md5=d8613d4835086ebc5e5c31b470ffc71e https://irepository.uniten.edu.my/handle/123456789/24906 11 3 232 239 All Open Access, Bronze, Green Penerbit UTHM Scopus |
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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 exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. 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-BFOA shows higher forecast accuracy with lower complexity than the existing models. � Universiti Tun Hussein Onn Malaysia Publisher's Office. |
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56602467500 |
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56602467500 Razak I.A.W.A. Ibrahim N.N.A.N. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. |
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Razak I.A.W.A. Ibrahim N.N.A.N. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. |
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Razak I.A.W.A. Ibrahim N.N.A.N. Abidin I.Z. Siah Y.K. Abidin A.A.Z. Rahman T.K.A. A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| author_sort |
Razak I.A.W.A. |
| title |
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| title_short |
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| title_full |
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| title_fullStr |
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| title_full_unstemmed |
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| title_sort |
hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting |
| publisher |
Penerbit UTHM |
| publishDate |
2023 |
| _version_ |
1806427439132311552 |
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13.222552 |