Real time load forecasting and optimal dispatching of smart grid based on edge computing and deep learning

Deep learning (DL), as a core research direction in the field of artificial intelligence (AI), with its powerful automatic feature extraction capability and excellent data fitting performance, can efficiently analyze and process massive power grid operation data. In recent years, it has shown broad application prospects in the construction of smart grids. The efficient and stable operation of smart grids highly relies on accurate load forecasting and scientific optimization scheduling. This paper systematically studies the collaborative optimization mechanism of information network technology and AI in the smart grid system and innovatively proposes an integrated model of smart grid load forecasting and optimal dispatching integrating edge computing and DL technology. The model effectively captures the nonlinear characteristics and spatio-temporal correlation characteristics of load data through DL technology and realizes real-time response to power grid load changes by taking advantage of distributed processing advantages of edge computing. The results indicate that our model not only significantly improves the accuracy of load forecasting but also reduces the operating loss cost of the power grid through intelligent optimization scheduling algorithms.