Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM
The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.757385/full |
| _version_ | 1818843424548192256 |
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| author | Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma |
| author_facet | Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma |
| author_sort | Mao Yang |
| collection | DOAJ |
| description | The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method. |
| first_indexed | 2024-12-19T04:57:39Z |
| format | Article |
| id | doaj.art-b3c20a4522e440119efd5d93671ecd47 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-19T04:57:39Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-b3c20a4522e440119efd5d93671ecd472022-12-21T20:35:12ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-11-01910.3389/fenrg.2021.757385757385Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVMMao Yang0Tian Peng1Xin Su2Miaomiao Ma3Miaomiao Ma4Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, ChinaKey Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, ChinaKey Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, ChinaKey Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaThe periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.https://www.frontiersin.org/articles/10.3389/fenrg.2021.757385/fullmeteorological factorswavelet packet decompositionleast squares support vector machinethe improved generalized error mixture distributionshort-term probability interval prediction |
| spellingShingle | Mao Yang Tian Peng Xin Su Miaomiao Ma Miaomiao Ma Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM Frontiers in Energy Research meteorological factors wavelet packet decomposition least squares support vector machine the improved generalized error mixture distribution short-term probability interval prediction |
| title | Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM |
| title_full | Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM |
| title_fullStr | Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM |
| title_full_unstemmed | Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM |
| title_short | Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM |
| title_sort | short term photovoltaic power interval prediction based on the improved generalized error mixture distribution and wavelet packet lssvm |
| topic | meteorological factors wavelet packet decomposition least squares support vector machine the improved generalized error mixture distribution short-term probability interval prediction |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.757385/full |
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