Forecasting Method of Photovoltaic Output Power Based on k-Means/KPCA/PSO-BP

In order to improve the forecasting accuracy of photovoltaic (PV) output power, a forecasting method is proposed based on k-means, KPCA and PSO-BP. Firstly, wavelet threshold de-noising algorithm is used to pretreat PV output data. Then, the k-means clustering algorithm is applied to divide the forecasting model into four sub models under different modes. The Kernel Principal Component Analysis (KPCA) method is used to reduce the dimensionality of the input space. Neural network algorithm is optimized based on Particle Swarm Optimization (PSO). Finally, the PV output power forecasting model based on k-means /KPCA/PSO-BP is established. The example data is used to verify the forecasting model, the results show that it can forecast the PV output power accurately in different modes and have good forecasting performance.