Photovoltaic power prediction based on hybrid modeling of neural network and stochastic differential equation

Abstract

With the increasing influence of new energy power system, the prediction of Photovoltaic (PV) output power becomes more and more important In this paper, it is the first time to put forward a hybrid modeling method combining long–short term memory recurrent neural network (LSTM) and stochastic differential equation (SDE). This method realizes the prediction of PV output power in different seasons and overcomes the uncertainty of PV power generation. Wavelet analysis and automatic encoder are used to decompose data and extract important features. According to the detailed signal sequence and the approximate signal sequence, the LSTM prediction model is established. Meanwhile, the mathematical model of SDE is established according to the detailed signal sequence. Finally, the output sequences of the two models are reconstructed by wavelet transform. This hybrid model can not only realize the point prediction of PV output power according to the predicted mean value, but also achieve the interval prediction under different confidence levels according to the randomness. In this paper, the proposed method is applied to predict the PV output power of CHINT photovoltaic power generation system with installed capacity of 10MW in different seasons, and the weather forecast data with errors of ±10%, ±20% and ±30% are used. Experimental results prove the effectiveness of the method. In the summer model considering forecast errors within ±20% of weather forecast data, the RMSEs of BP neural network, LSTM and convolutional neural network (CNN) are 5.9468, 5.6762 and 5.8004 respectively. However, the RMSE of the mean prediction with the confidence level of 90% under the proposed method is 4.4647. With this method, the results of interval prediction and point prediction of PV output power can provide better decision support for the stable and safe operation of PV grid connection. They have higher reference value for energy dispatching departments.