A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares su...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/4/697 |
| _version_ | 1798002599832584192 |
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| author | Peng Lu Lin Ye Bohao Sun Cihang Zhang Yongning Zhao Tengjing Zhu |
| author_facet | Peng Lu Lin Ye Bohao Sun Cihang Zhang Yongning Zhao Tengjing Zhu |
| author_sort | Peng Lu |
| collection | DOAJ |
| description | Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models. |
| first_indexed | 2024-04-11T11:54:49Z |
| format | Article |
| id | doaj.art-fa6d08c2ec5a48f1ac0faddd732c14a3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:54:49Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fa6d08c2ec5a48f1ac0faddd732c14a32022-12-22T04:25:13ZengMDPI AGEnergies1996-10732018-03-0111469710.3390/en11040697en11040697A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSAPeng Lu0Lin Ye1Bohao Sun2Cihang Zhang3Yongning Zhao4Tengjing Zhu5College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaChina Electric Power Research Institute, Haidian District, Beijing 100192, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaWind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.http://www.mdpi.com/1996-1073/11/4/697wind power predictionensemble empirical mode decomposition-permutation entropy (EEMD-PE)least squares support vector machine (LSSVM)heuristic algorithm |
| spellingShingle | Peng Lu Lin Ye Bohao Sun Cihang Zhang Yongning Zhao Tengjing Zhu A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA Energies wind power prediction ensemble empirical mode decomposition-permutation entropy (EEMD-PE) least squares support vector machine (LSSVM) heuristic algorithm |
| title | A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA |
| title_full | A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA |
| title_fullStr | A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA |
| title_full_unstemmed | A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA |
| title_short | A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA |
| title_sort | new hybrid prediction method of ultra short term wind power forecasting based on eemd pe and lssvm optimized by the gsa |
| topic | wind power prediction ensemble empirical mode decomposition-permutation entropy (EEMD-PE) least squares support vector machine (LSSVM) heuristic algorithm |
| url | http://www.mdpi.com/1996-1073/11/4/697 |
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