Electric Vehicle Charging Price Forecasting Based on Regional Electricity Demand Over Cross-Scale Adaptive Weighted Fusion Network

With the acceleration of global energy transition and the popularization of Electric Vehicles (EVs), regional electricity demand and EV charging price forecasting has become an important issue in power system management. In this paper, a Cross-Scale Adaptive Weighted Fusion Network(CSAWFNet) based on triple weighted fusion LSTM model is proposed to address the limitations of the existing forecasting methods in dealing with the complex and changing power demand and price fluctuations. The feature extraction was first performed using machine learning algorithms, and then as inputs to the model. The model can effectively capture the short-term high-frequency changes and long-term trends of electricity demand and price fluctuations through the improvements of sliding window, multi-scale memory fusion mechanism, adaptive memory pruning mechanism, and cross-layer connections. The experimental re-sults show that CSAWFNet outperforms traditional models in forecasting regional electricity demand and EV charging prices, particularly during peak and trough load periods and price fluctuations.