Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion

Abstract

This study focuses on ensuring the stable operation of the power grid by accurately forecasting the theoretical power generation capacity of wind turbine units, especially in scenarios integrating significant amounts of renewable energy into the grid. The forecasting process involves two key steps: initially forecasting wind speeds and then estimating theoretical power generation using wind turbine power conversion curves. This article proposes a wind speed forecasting system based on deep learning, integrating multiple hybrid models and employing deep learning algorithms to select the optimal wind speed hybrid forecasting model, optimized by the multi-objective mayfly optimization algorithm. Additionally, a wind energy conversion simulation system for wind turbines has been developed, precisely simulating the physical process of converting wind energy into electrical energy. This system, in conjunction with wind speed forecasting, estimates the theoretical power generation of wind farms. The results of this research hold significant practical implications for enhancing the operational efficiency of wind power, strengthening the grid's supply-demand balance, and increasing the economic and environmental value of wind power projects.