Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Consi...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/292575 |
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| _version_ | 1832563059531448320 |
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| author | Zhongyi Hu Yukun Bao Tao Xiong |
| author_facet | Zhongyi Hu Yukun Bao Tao Xiong |
| author_sort | Zhongyi Hu |
| collection | DOAJ |
| description | Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. |
| format | Article |
| id | doaj-art-52893696de814d518fa014da03a7e22d |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-52893696de814d518fa014da03a7e22d2025-02-03T01:21:06ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/292575292575Electricity Load Forecasting Using Support Vector Regression with Memetic AlgorithmsZhongyi Hu0Yukun Bao1Tao Xiong2Department of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaElectricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.http://dx.doi.org/10.1155/2013/292575 |
| spellingShingle | Zhongyi Hu Yukun Bao Tao Xiong Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms The Scientific World Journal |
| title | Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms |
| title_full | Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms |
| title_fullStr | Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms |
| title_full_unstemmed | Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms |
| title_short | Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms |
| title_sort | electricity load forecasting using support vector regression with memetic algorithms |
| url | http://dx.doi.org/10.1155/2013/292575 |
| work_keys_str_mv | AT zhongyihu electricityloadforecastingusingsupportvectorregressionwithmemeticalgorithms AT yukunbao electricityloadforecastingusingsupportvectorregressionwithmemeticalgorithms AT taoxiong electricityloadforecastingusingsupportvectorregressionwithmemeticalgorithms |