Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting
Forecasting price has now become 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 increasing the utility's profit and energy efficiency as well. The main challenge in for...
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Universitatea Politehnica din Timisoara
2023
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my.uniten.dspace-224632023-05-29T14:01:08Z Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting Intan Azmira W.A.R. Izham Z.A. Keem Siah Y. Titik Khawa A.R. 56602467500 35606640500 24448864400 57035448200 Forecasting price has now become 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 increasing the utility's profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide. Final 2023-05-29T06:01:08Z 2023-05-29T06:01:08Z 2015 Article 2-s2.0-84952937786 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952937786&partnerID=40&md5=b4635e6cf680a9bb0369e214674404f9 https://irepository.uniten.edu.my/handle/123456789/22463 15 1 159 166 Universitatea Politehnica din Timisoara Scopus |
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Forecasting price has now become 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 increasing the utility's profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide. |
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56602467500 Intan Azmira W.A.R. Izham Z.A. Keem Siah Y. Titik Khawa A.R. |
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Article |
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Intan Azmira W.A.R. Izham Z.A. Keem Siah Y. Titik Khawa A.R. |
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Intan Azmira W.A.R. Izham Z.A. Keem Siah Y. Titik Khawa A.R. Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| author_sort |
Intan Azmira W.A.R. |
| title |
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| title_short |
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| title_full |
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| title_fullStr |
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| title_full_unstemmed |
Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting |
| title_sort |
feature selection and parameter optimization with ga-lssvm in electricity price forecasting |
| publisher |
Universitatea Politehnica din Timisoara |
| publishDate |
2023 |
| _version_ |
1806426207224332288 |
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13.222552 |