A Short-Term Power Prediction Method Based on Temporal Convolutional Network in Virtual Power Plant Photovoltaic System

Abstract

In the virtual power plant architecture based on edge computing, short-term photovoltaic power prediction is carried out by using multi-source data of photovoltaic power plants stored in photovoltaic intelligent edge terminal (PVIET), which can achieve efficient dispatching and management of renewable energy by the control and coordination center, thus ensuring the safe and stable operation of the power grid. Therefore, this paper proposes a short-term photovoltaic power prediction method that combines improved grey relation analysis (IGRA), efficient channel attention module (ECANet), and temporal convolutional network (TCN). The IGRA algorithm can fully explore the correlation between predicted days and historical days using meteorological environmental data from multiple sources, while the deep TCN model combined with ECANet can effectively avoid the drawbacks of traditional photovoltaic power prediction models. Finally, using the real case of Alice Spring in Australia, the proposed method (IGRA-ECA-TCN) is compared with other hybrid models in different seasons and weather conditions, and the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are selected as the evaluation indicators of algorithm performance. The results indicate that IGRA-ECA-TCN has stronger adaptability and higher accuracy.