Bayesian Optimization with Residual Correction for wind Power Clusters Output Forecasting and Comprehensive Benefit

With the rapid increase of installed wind power capacity, the intermittency and volatility of its power pose serious challenges to grid stability. In this paper, a hybrid prediction framework integrating signal decomposition, deep learning and residual correction was proposed for the problems of data quality, poor model adaptability and lack of a comprehensive benefit assessment system in wind power forecasting in China. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract multi-scale features from the wind power data, a long-and short-term memory network (LSTM) prediction model based on Bayesian optimization is established and combined with the Random Forest residual correction method to compensate the prediction errors dynamically, and a comprehensive evaluation system containing environmental and social benefits is constructed based on the hierarchical analysis method to quantify the comprehensive value of the prediction algorithm. The empirical study shows that the optimized LSTM model reduces the root mean square error (RMSE) to 3.66 MW on the test set, and the coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) is improved to 0.9596, which is a 27.6% improvement in accuracy over the traditional model, and through the validation of the joint prediction of multiple wind farms, the comprehensive benefit score is improved by 63.2% over the benchmark model. The method proposed in this paper provides a high-precision and low-error technical path for wind power forecasting, which is of great practical significance for promoting the low-carbon transformation of energy structure.