Application of SRNN-GRU in Photovoltaic power Forecasting

Abstract

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction.