Research on short-term module temperature prediction model based on BP neural network for photovoltaic power forecasting

As one of the most related parameters of photovoltaic (PV) power generation, the temperature of PV modules and its prediction play very important role in PV power forecasting. A short-term step-wise temperature prediction model for PV module based on back propagation (BP) neural network is proposed in this paper. Firstly, the impact factors of PV module temperature are determined according to the PV module physical characteristics and the correlation coefficient. Secondly, two different prediction methods, direct and step-wise modeling methods based on BP neural network are applied to build the prediction models respectively. Thirdly, the mapping models between the module temperature and the impact factors for step-wise prediction are established under each weathers types. Finally, the deviations of two different kinds of prediction models are analyzed and discussed using actual operation data. The results indicate that, other things equal, the step-wise prediction model has better accuracy than the direct prediction model.