Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization

To improve the accuracy of midterm power load forecasting, a forecasting model is proposed by combing kernel principal component analysis (KPCA) with back propagation neural network. First, the dimension of the input space is reduced by KPCA, then input the data set to the neural network model, optimized by particle swarm optimization. The monthly average of daily peak loads is forecasted to modify the daily forecast values and output the daily peak load in the end. Using the data provided by European Network on Intelligent Technologies to test the model, the mean absolute percent error of load forecasting model is only 1.39%. The feasibility and validity of the model have been proven.