Offshore wind power capacity prediction based on integrated optimization model

With the rapid development of renewable energy worldwide, offshore wind power, as an important form of green energy, has received widespread attention. In order to improve the power generation efficiency and prediction accuracy of offshore wind power, this paper proposes an offshore wind power capacity prediction method based on an integrated optimization model. This method combines multiple machine learning algorithms and effectively integrates the advantages of different models through ensemble modeling to improve the accuracy and stability of predictions. Specifically, in this study, the influencing factors of offshore wind power production capacity were analyzed, and various meteorological parameters such as wind speed, wind direction, temperature, and humidity were selected as input features. Then, multiple base learning algorithms such as LightGBM, XGBoost, and Gradient Boosting were used, and integrated optimization was performed through weighted averaging to form the final prediction model. During the model training process, in order to further improve the prediction accuracy, the particle swarm optimization (PSO) algorithm was used to optimize the model parameters. The experimental results show that the integrated optimization model performs better than the traditional single model in offshore wind power capacity prediction, has higher prediction accuracy and stronger generalization ability, and can effectively support the operation management and energy dispatching decision-making of offshore wind farms. Finally, this article also discusses the feasibility and development prospects of this method in practical applications, providing theoretical basis and technical support for the intelligent and efficient development of the offshore wind power industry.