基于GSABO-BP和Bootstrap的电力负荷区间预测OA北大核心CSTPCD

In allusion to the issue of high volatility in electricity load sequences and low predictive accuracy,an electricity load interval prediction method based on the GSABO-BP(golden sine algorithm-subtraction average based optimizer-back propagation)model and Bootstrap is proposed.An improved GSABO algorithm is proposed,which can integrate the golden sine algorithm(Gold-SA)to enhance its search ability while preserving the good convergence of the subtraction average based optimizer(SABO).The proposed method is used to optimize the weights and thresholds of the BP neural network,constructing a GSABO-BP prediction model for point prediction of power load.The Bootstrap method is used to analyze the error of power load prediction,and determine the fluctuation range of the output results by combining with the point prediction results.The simulation testing shows that the proposed method has strong optimization ability and good robustness.In comparison with other algorithms,this method has significantly improved in prediction accuracy,interval reliability,interval width,etc.The combination of point prediction and interval prediction results can help accurately evaluate prediction errors and has high practical application value.