A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. (1st October 2019) - Wang, Kejun; Qi, Xiaoxia; Liu, Hongda
Highlights: Three deep learning neural networks are proposed for photovoltaic power forecasting. The influence of input sequence length on prediction accuracy is considered. Provide suggestions for choosing the most suitable network in practical application. The better effect of deep learning on photovoltaic power forecasting is demonstrated. The stability and robustness of the proposed model are proved. Abstract: Accurate photovoltaic power forecasting is of great help to the operation of photovoltaic power generation system. However, due to the instability, intermittence, and randomness of solar energy, accurate prediction of photovoltaic power forecasting becomes very difficult. In this paper, the convolutional neural network, long short-term memory network, and hybrid model based on convolutional neural network and long short-term memory network models were proposed, and are applied to the obtained data in DKASC, Alice Springs photovoltaic system. The mean absolute percentage error, root mean square error, and mean absolute error indicators are used to evaluate the performance of the prediction model in this paper. The results showed that when the input sequence is increased, the accuracy of the model is also improved, and the prediction effect of the hybrid model is the best, followed by that of convolutional neural network. While long short-term memory network had the worst prediction effect, the training time was the shortest. However, not the longer the input sequence is, the better the prediction will be. This may be related to the characteristics of the time series itself. The results of the deep learning model proposed in this paper on photovoltaic power prediction also indicate that deep learning is very helpful for improving the accuracy of PV power prediction.