Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network

Abstract

Under the background of the global pursuit of carbon neutrality, the trend of photovoltaic power generation replacing traditional thermal power generation is increasingly apparent. To improve the performance of the model in photovoltaic power forecasting, this study proposed a novel deep learning-based model named PLSTNet for ultra-short-term prediction of photovoltaic power over a 5 min time span. This model is a novel dual-path prediction. On one hand, it effectively captures short-term fluctuations in time series data by combining CNN and RNN. On the other hand, it further captures and analyzes long-term trends in fluctuations through the use of a smoothing layer and RNN's recurrent skip layer. In one-step and multi-step forecasting experiments on annual and seasonal datasets, we compared the performance of the PLSTNet model with LSTNet, PHILNet, TCN_GRU, and ResCNN to assess its performance. In one-step and multi-step forecasting using the annual dataset, the MAE of the PLSTNet model is at least 15.5% lower than that of other models. Similarly, for seasonal datasets, the MAE of the PLSTNet model is at least 13.2% lower than other models. The experimental results demonstrate that in various photovoltaic power forecasting scenarios, the PLSTNet model has achieved higher accuracy in ultra-short-term predictions.