Design of Offshore Wind Power ROI Energy Analysis Model Using XGBoost Algorithm

Against the backdrop of increasing demand for renewable energy in countries around the world, offshore wind power has become a major clean energy source. Effective cost-benefit evaluation of its maintenance operations is the key to ensuring its economic operation. This article aims to establish a maintenance cost-benefit analysis model based on the XGBoost (eXtreme Gradient Boosting) algorithm, through a comprehensive analysis of wind turbine maintenance costs and power generation benefits, to achieve accurate prediction and optimization of the maintenance operating cost-benefit ratio. Specifically, this article can collect relevant information such as wind speed, direction, maintenance records, maintenance costs, and power generation, and conduct deep learning and analysis based on the XGBoost algorithm to establish a mathematical model that can achieve dynamic calculation and optimization of energy efficiency ratio. The experimental results show that as the average wind speed increases, the ROI (Return on Investment) shows an upward trend. For example, WF006 has the highest average wind speed (9.0m/s) and the highest ROI (0.16). The research results of this article can help to improve the operating efficiency and economic benefits of China's offshore wind farms, and can help to improve the level of sustainable development of China's wind power industry.