Research on neural network wind speed prediction model based on improved sparrow algorithm optimization
The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722019266 |
| _version_ | 1797950767800254464 |
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| author | Liang Zhang Shan He Jing Cheng Zhi Yuan Xueqing Yan |
| author_facet | Liang Zhang Shan He Jing Cheng Zhi Yuan Xueqing Yan |
| author_sort | Liang Zhang |
| collection | DOAJ |
| description | The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy. |
| first_indexed | 2024-04-10T22:20:21Z |
| format | Article |
| id | doaj.art-e9c8ca3ace3641cd8122236c0731b606 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:20:21Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-e9c8ca3ace3641cd8122236c0731b6062023-01-18T04:31:48ZengElsevierEnergy Reports2352-48472022-11-018739747Research on neural network wind speed prediction model based on improved sparrow algorithm optimizationLiang Zhang0Shan He1Jing Cheng2Zhi Yuan3Xueqing Yan4School of Electric Engineering, Xinjiang University, Urumqi 830049, ChinaSchool of Electric Engineering, Xinjiang University, Urumqi 830049, China; Engineering Research Center of Ministry of Education for Renewable Energy Power Generation and Grid Connection Control, Urumqi 830049, China; Correspondence to: No. 777, Huarui Street, Shuimoogou District, Urumqi City, Xinjiang Uygur Autonomous Region, China.School of Electric Engineering, Xinjiang University, Urumqi 830049, China; Engineering Research Center of Ministry of Education for Renewable Energy Power Generation and Grid Connection Control, Urumqi 830049, ChinaSchool of Electric Engineering, Xinjiang University, Urumqi 830049, China; Engineering Research Center of Ministry of Education for Renewable Energy Power Generation and Grid Connection Control, Urumqi 830049, ChinaSchool of Electric Engineering, Xinjiang University, Urumqi 830049, ChinaThe wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2352484722019266Neural networkWind speed predictionImproved sparrow algorithm optimization |
| spellingShingle | Liang Zhang Shan He Jing Cheng Zhi Yuan Xueqing Yan Research on neural network wind speed prediction model based on improved sparrow algorithm optimization Energy Reports Neural network Wind speed prediction Improved sparrow algorithm optimization |
| title | Research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| title_full | Research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| title_fullStr | Research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| title_full_unstemmed | Research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| title_short | Research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| title_sort | research on neural network wind speed prediction model based on improved sparrow algorithm optimization |
| topic | Neural network Wind speed prediction Improved sparrow algorithm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722019266 |
| work_keys_str_mv | AT liangzhang researchonneuralnetworkwindspeedpredictionmodelbasedonimprovedsparrowalgorithmoptimization AT shanhe researchonneuralnetworkwindspeedpredictionmodelbasedonimprovedsparrowalgorithmoptimization AT jingcheng researchonneuralnetworkwindspeedpredictionmodelbasedonimprovedsparrowalgorithmoptimization AT zhiyuan researchonneuralnetworkwindspeedpredictionmodelbasedonimprovedsparrowalgorithmoptimization AT xueqingyan researchonneuralnetworkwindspeedpredictionmodelbasedonimprovedsparrowalgorithmoptimization |