Memory long and short term time series network for ultra-short-term photovoltaic power forecasting

Abstract

Photovoltaic (PV) power is stochastic, intermittent and volatile, which has brought huge challenges to the safe and stable operation of the power grid. Accurate PV power forecasting is becoming a significant task for PV plant grid connecting, scheduling and guaranteeing the safety of the power grid. To improve the accuracy of PV power forecasting, a memory long and short term time series network (MLSTNet) model is proposed to perform ultra-short-term PV power forecasting with a time horizon varying from 15 min to 4 h. Firstly, the input variables are screened by calculating Spearman correlation coefficients between weather data. To cluster different types of data, the classification coefficient is set. Secondly, different type data are automatically identified in the MLSTNet model. The appropriate model parameters and network structures are adjusted for the higher forecasting speed and accuracy. By using the single-step rolling input method and the temporal attention convolutional neural network, the ability of temporal and spatial feature extraction has been significantly enhanced, allowing real-time forecasting and dynamic optimization of the proposed model. Thirdly, long-term and short-term historical data are fed into the model, and different periods dependencies are captured while incorporating multiple variable dimensions, leading to more memorable in the network. Finally, the experimental data is selected from the actual operation data of a PV plant in northern China, which represents the universal applicability and practical application of the model. The performance of the proposed model is demonstrated with actual PV plant dataset, the RMSE of proposed model decreased by 32.73%, 5.44%, 12.44%, and 31.22%. Results indicated that it is feasible to use the proposed MLSTNet model for time series forecasting. The core idea of the MLSTNet model is to achieve higher accuracy in ultra-short-term forecasting by enhancing the ability to extract temporal features and linearity in the model, providing a new idea for deep learning-based methods for ultra-short-term PV power forecasting.