Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method
Abstract To satisfy the grid operation scheduling requirements for wind power forecasting model accuracy, the measured wind speed near the height of the wind turbine hub is added to the wind power combined forecasting model. First, the relationship between the numerical weather prediction wind speed...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-05-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12053 |
| _version_ | 1811266862935179264 |
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| author | Mao Yang Bozhi Dai Jinxin Wang Xinxin Chen Yong Sun Baoju Li |
| author_facet | Mao Yang Bozhi Dai Jinxin Wang Xinxin Chen Yong Sun Baoju Li |
| author_sort | Mao Yang |
| collection | DOAJ |
| description | Abstract To satisfy the grid operation scheduling requirements for wind power forecasting model accuracy, the measured wind speed near the height of the wind turbine hub is added to the wind power combined forecasting model. First, the relationship between the numerical weather prediction wind speed and the measured wind speed at different heights are analysed, and the correlation between each wind speed and the wind power is compared. Second, the random forest algorithm combined with the cumulative contribution rate is used to select several meteorological types of numerical weather prediction data as the input of the long short‐term memory network to predict wind speed. Third, while inputting the meteorological data provided by numerical weather prediction, which is highly related to wind power, the wind power prediction network also uses the predicted wind speed of the upper network as input to predict wind power. Finally, the entropy method is used to dynamically determine the combined weights of each forecasting model and improve the adaptability of the model. Research and analysis using measured data from two wind farms located in northeast China have verified the effectiveness of the method. |
| first_indexed | 2024-04-12T20:50:59Z |
| format | Article |
| id | doaj.art-007fb0d0edec493d8956606c90bcabcc |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-12T20:50:59Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-007fb0d0edec493d8956606c90bcabcc2022-12-22T03:17:07ZengWileyIET Renewable Power Generation1752-14161752-14242021-05-011571358136810.1049/rpg2.12053Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy methodMao Yang0Bozhi Dai1Jinxin Wang2Xinxin Chen3Yong Sun4Baoju Li5Modern Power System Simulation Control & Renewable Energy Technology Key Laboratory of the Ministry of Education Northeast Electric Power University Changchun Street, No. 169 Jilin ChinaModern Power System Simulation Control & Renewable Energy Technology Key Laboratory of the Ministry of Education Northeast Electric Power University Changchun Street, No. 169 Jilin ChinaModern Power System Simulation Control & Renewable Energy Technology Key Laboratory of the Ministry of Education Northeast Electric Power University Changchun Street, No. 169 Jilin ChinaPingdingshan Power Supply Company State Grid Henan Power Company No. 6, South Section of Xinhua Road Shareholding Pingdingshan ChinaState Grid Jilin Electric Power Supply Company N0. 4629, People Street Changchun ChinaState Grid Jilin Electric Power Supply Company N0. 4629, People Street Changchun ChinaAbstract To satisfy the grid operation scheduling requirements for wind power forecasting model accuracy, the measured wind speed near the height of the wind turbine hub is added to the wind power combined forecasting model. First, the relationship between the numerical weather prediction wind speed and the measured wind speed at different heights are analysed, and the correlation between each wind speed and the wind power is compared. Second, the random forest algorithm combined with the cumulative contribution rate is used to select several meteorological types of numerical weather prediction data as the input of the long short‐term memory network to predict wind speed. Third, while inputting the meteorological data provided by numerical weather prediction, which is highly related to wind power, the wind power prediction network also uses the predicted wind speed of the upper network as input to predict wind power. Finally, the entropy method is used to dynamically determine the combined weights of each forecasting model and improve the adaptability of the model. Research and analysis using measured data from two wind farms located in northeast China have verified the effectiveness of the method.https://doi.org/10.1049/rpg2.12053Wind energyWinds and their effects in the lower atmosphereWeather analysis and predictionWind power plantsOther topics in statisticsOptimisation techniques |
| spellingShingle | Mao Yang Bozhi Dai Jinxin Wang Xinxin Chen Yong Sun Baoju Li Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method IET Renewable Power Generation Wind energy Winds and their effects in the lower atmosphere Weather analysis and prediction Wind power plants Other topics in statistics Optimisation techniques |
| title | Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| title_full | Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| title_fullStr | Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| title_full_unstemmed | Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| title_short | Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| title_sort | day ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method |
| topic | Wind energy Winds and their effects in the lower atmosphere Weather analysis and prediction Wind power plants Other topics in statistics Optimisation techniques |
| url | https://doi.org/10.1049/rpg2.12053 |
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