A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port
Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/9/1636 |
| _version_ | 1797579411338297344 |
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| author | Yong Gu Wenhao Xu Daogui Tang Yuji Yuan Ziyi Chai Yao Ke Josep M. Guerrero |
| author_facet | Yong Gu Wenhao Xu Daogui Tang Yuji Yuan Ziyi Chai Yao Ke Josep M. Guerrero |
| author_sort | Yong Gu |
| collection | DOAJ |
| description | Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed. |
| first_indexed | 2024-03-10T22:36:47Z |
| format | Article |
| id | doaj.art-fb2de3a79eb047aba0d74119a151c403 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T22:36:47Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fb2de3a79eb047aba0d74119a151c4032023-11-19T11:25:18ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-08-01119163610.3390/jmse11091636A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan PortYong Gu0Wenhao Xu1Daogui Tang2Yuji Yuan3Ziyi Chai4Yao Ke5Josep M. Guerrero6School of Transportation and Logistics Engineering, Wuhan University of Technology, 1178 Heping Street, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, 1178 Heping Street, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, 1178 Heping Street, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, 1178 Heping Street, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, 1178 Heping Street, Wuhan 430063, ChinaNingbo Beilun Third Container Terrminal Co., Ltd., 8 Jixiang Road, Ningbo 315813, ChinaCenter for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg East, DenmarkWind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed.https://www.mdpi.com/2077-1312/11/9/1636green portwind energywind speed predictionSSAWNN |
| spellingShingle | Yong Gu Wenhao Xu Daogui Tang Yuji Yuan Ziyi Chai Yao Ke Josep M. Guerrero A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port Journal of Marine Science and Engineering green port wind energy wind speed prediction SSA WNN |
| title | A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port |
| title_full | A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port |
| title_fullStr | A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port |
| title_full_unstemmed | A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port |
| title_short | A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port |
| title_sort | combined wind forecasting model based on ssa and wnn application on real case of ningbo zhoushan port |
| topic | green port wind energy wind speed prediction SSA WNN |
| url | https://www.mdpi.com/2077-1312/11/9/1636 |
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