Short-term photovoltaic output power prediction based on similar day and optimized BP neural network

The photovoltaic (PV) output power is affected by the ambient temperature, seasons, weather and other factors, which makes the PV output power very unstable. Therefore, accurate prediction of the PV output power is highly beneficial. This paper is dedicated to finding a simple and reliable PV short-term output power prediction method. First, we choose four key parameters, season, solar irradiance, temperature and relative humidity, to predict PV output power by using the similar day theory, which is mainly because the above four parameters are decisive for PV output power, although more parameters being taken into account will make the prediction accuracy higher, but it brings along with it an increase in the complexity; secondly, we choose the backpropagation (BP) neural network because it is very suitable for the PV output power prediction due to its excellent learning ability; finally, we optimize the standard BP neural network in loss functions, activation functions and optimizers to further improve its prediction accuracy. We validate the proposed method in different seasons and under other weather conditions. The results show that the proposed method has better prediction results, the optimized BP neural network has better performance compared with the standard BP neural network, and the standard deviation of the prediction is improved from similar to 1382, similar to 1571, similar to 1457, similar to 989 to similar to 903, similar to 792, similar to 333 and similar to 409.