A Combined Wind Forecasting Model Based on SSA and WNN: Application on Real Case of Ningbo Zhoushan Port

• Published in: Journal of marine science and engineering Vol. 11; no. 9; p. 1636
• Main Authors: Gu, Yong, Xu, Wenhao, Tang, Daogui, Yuan, Yuji, Chai, Ziyi, Ke, Yao, Guerrero, Josep M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Accuracy; Air quality management; Algorithms; Alternative energy sources; Buildings and facilities; Carbon; Electric power production; Emissions; Emissions control; Energy utilization; Fitness; Food; Foraging behavior; Forecasts and trends; green port; Green technology; Intermodal transportation; Machine learning; Mathematical models; Neural networks; Optimization; Optimization algorithms; Parameters; Port operations; Ports; Prediction models; Search algorithms; Solar energy; SSA; Statistical methods; Water transportation; wind energy; Wind measurement; Wind power; Wind power generation; Wind speed; wind speed prediction; WNN; China
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11091636

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.