A novel method of BFOA-LSSVM for electricity price forecasting
Forecasting price has now become an essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increase the utilitys profit and energy efficiency. This paper proposes a novel method of...
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Asian Research Publishing Network
2023
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my.uniten.dspace-227402023-05-29T14:11:57Z A novel method of BFOA-LSSVM for electricity price forecasting Razak I.A.W.A. Abidin I.Z. Yap K.S. Abidin A.A.Z. Rahman T.K.A. Ahmad A. 56602467500 35606640500 24448864400 25824750400 8922419700 55336187300 Forecasting price has now become an essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increase the utilitys profit and energy efficiency. This paper proposes a novel method of Least Square Support Vector Machine (LSSVM) with Bacterial Foraging Optimization Algorithm (BFOA) to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameters held by BFOA are proven to improve accuracy as well as efficiency of prediction. A comparative study of the proposed method with previous researches was conducted in term of forecast accuracy. The results indicate that (1) the LSSVM with BFOA outperforms other methods for same test data; (2) the optimization algorithm of BFOA gives better accuracy than other optimization techniques. In fact, the proposed approach is less complex compared to other methods presented in this paper. � 2006-2016 Asian Research Publishing Network (ARPN). Final 2023-05-29T06:11:57Z 2023-05-29T06:11:57Z 2016 Article 2-s2.0-84965081612 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965081612&partnerID=40&md5=ae145de1c00c1df3262dc7f868bc6354 https://irepository.uniten.edu.my/handle/123456789/22740 11 8 4961 4968 Asian Research Publishing Network Scopus |
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Forecasting price has now become an essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increase the utilitys profit and energy efficiency. This paper proposes a novel method of Least Square Support Vector Machine (LSSVM) with Bacterial Foraging Optimization Algorithm (BFOA) to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameters held by BFOA are proven to improve accuracy as well as efficiency of prediction. A comparative study of the proposed method with previous researches was conducted in term of forecast accuracy. The results indicate that (1) the LSSVM with BFOA outperforms other methods for same test data; (2) the optimization algorithm of BFOA gives better accuracy than other optimization techniques. In fact, the proposed approach is less complex compared to other methods presented in this paper. � 2006-2016 Asian Research Publishing Network (ARPN). |
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56602467500 |
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56602467500 Razak I.A.W.A. Abidin I.Z. Yap K.S. Abidin A.A.Z. Rahman T.K.A. Ahmad A. |
| format |
Article |
| author |
Razak I.A.W.A. Abidin I.Z. Yap K.S. Abidin A.A.Z. Rahman T.K.A. Ahmad A. |
| spellingShingle |
Razak I.A.W.A. Abidin I.Z. Yap K.S. Abidin A.A.Z. Rahman T.K.A. Ahmad A. A novel method of BFOA-LSSVM for electricity price forecasting |
| author_sort |
Razak I.A.W.A. |
| title |
A novel method of BFOA-LSSVM for electricity price forecasting |
| title_short |
A novel method of BFOA-LSSVM for electricity price forecasting |
| title_full |
A novel method of BFOA-LSSVM for electricity price forecasting |
| title_fullStr |
A novel method of BFOA-LSSVM for electricity price forecasting |
| title_full_unstemmed |
A novel method of BFOA-LSSVM for electricity price forecasting |
| title_sort |
novel method of bfoa-lssvm for electricity price forecasting |
| publisher |
Asian Research Publishing Network |
| publishDate |
2023 |
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1806427411850461184 |
| score |
13.211869 |