Short-Term Load Forecasting Based on Feature Selection and Combination Model
A short-term load forecasting method based on feature selection and combination model is proposed. At first, the method divides the feature vectors into two sets according to the individual characteristics. Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-07-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202111045 |
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| _version_ | 1850225584351739904 |
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| author | Yusong XU Shanhua ZOU Xianling LU |
| author_facet | Yusong XU Shanhua ZOU Xianling LU |
| author_sort | Yusong XU |
| collection | DOAJ |
| description | A short-term load forecasting method based on feature selection and combination model is proposed. At first, the method divides the feature vectors into two sets according to the individual characteristics. Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm are individually employed for selection. Bayesian information criterion is used to get the dimension of the optimal feature vector. And then, three different simple-kernel based support vector regression models are built using three kernel functions respectively and complete prediction. Finally, a neural network is set up for experimental analysis. The simulation results show that the proposed combination model has a great high forecasting accuracy and robustness. |
| format | Article |
| id | doaj-art-7da795b8acf141eaa673ace8b19b662a |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-7da795b8acf141eaa673ace8b19b662a2025-08-20T02:05:19ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-07-0155712112710.11930/j.issn.1004-9649.202111045zgdl-55-06-xuyusongShort-Term Load Forecasting Based on Feature Selection and Combination ModelYusong XU0Shanhua ZOU1Xianling LU2Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, ChinaJiangsu Key Construction Laboratory of IoT Application Technology, Wuxi 214100, ChinaKey Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, ChinaA short-term load forecasting method based on feature selection and combination model is proposed. At first, the method divides the feature vectors into two sets according to the individual characteristics. Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm are individually employed for selection. Bayesian information criterion is used to get the dimension of the optimal feature vector. And then, three different simple-kernel based support vector regression models are built using three kernel functions respectively and complete prediction. Finally, a neural network is set up for experimental analysis. The simulation results show that the proposed combination model has a great high forecasting accuracy and robustness.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202111045short-term load forecastingsupport vector regressionshallow neural networkcombination model |
| spellingShingle | Yusong XU Shanhua ZOU Xianling LU Short-Term Load Forecasting Based on Feature Selection and Combination Model Zhongguo dianli short-term load forecasting support vector regression shallow neural network combination model |
| title | Short-Term Load Forecasting Based on Feature Selection and Combination Model |
| title_full | Short-Term Load Forecasting Based on Feature Selection and Combination Model |
| title_fullStr | Short-Term Load Forecasting Based on Feature Selection and Combination Model |
| title_full_unstemmed | Short-Term Load Forecasting Based on Feature Selection and Combination Model |
| title_short | Short-Term Load Forecasting Based on Feature Selection and Combination Model |
| title_sort | short term load forecasting based on feature selection and combination model |
| topic | short-term load forecasting support vector regression shallow neural network combination model |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202111045 |
| work_keys_str_mv | AT yusongxu shorttermloadforecastingbasedonfeatureselectionandcombinationmodel AT shanhuazou shorttermloadforecastingbasedonfeatureselectionandcombinationmodel AT xianlinglu shorttermloadforecastingbasedonfeatureselectionandcombinationmodel |