Modular-Echo State Network Applied on Forecasting of Photovoltaic Power Generation

Abstract

According to issues of the power system interconnection caused by uncertainty and intermittent of photo-voltaic (PV) power generation, a model based on modular-echo state network (M-ESN) is given to forecast power generation. Firstly, it is established forecast sub-models by modular neural networks (MNN) considering different seasons. Then, dividing each sub-model is based on similar days from history data of photovoltaic power generation with the average temperature as samples to train the model and generated power forecasting by the echo state network (ESN). Finally, integration output forecast numbers are gained. The results show that, compared with the ESN forecast model and BP forecast model, this prediction model has faster forecast speed, higher accuracy and better stability.