An improved method for PV output prediction using artificial neural network with overlap training range

Efficient and accurate photovoltaic (PV) modeling is significant for ensuring the stable operation of the power grid. The nonlinear output characteristics of PV modules are usually described by current-voltage (I-V) curves or power-voltage (P-V) curves. Traditional models normally include a diode model and an analytical model. The process of model parameter's extraction is complex and inconvenient because the equations of the diode model and analytical model are non-linear. In traditional extraction methods using a neural network, all irradiance training data are input into the neural network, which suffer from long training time and insufficient accuracy. In this paper, two neural networks with different training ranges are proposed to replace the whole neural network for predicting I-V curves, P-V curves, and maximum power of the analytical model. Each neural network consists of three layers. In detail, the input is the value of solar irradiance and module temperature, and the output is the shape parameters of the analytical model. The effectiveness of the proposed method is verified by massive experimental data of different PV modules. The output performance estimation of the proposed method is better than that of other methods.