Electric vehicle charging load forecasting method based on adaptive artificial fish swarm algorithm optimized Long short-term memory network

Electric vehicle (EV) charging load forecasting plays a crucial role in optimizing the utilization of energy resources and infrastructure planning. In this paper, we propose a novel method for EV charging load forecasting based on an adaptive artificial fish swarm (AAFS) algorithm optimized long short-term memory (LSTM) network. The AAFS algorithm is utilized to optimize the LSTM network parameters, enabling enhanced accuracy and efficiency in forecasting. The experimental results demonstrate that the AAFS-LSTM model outperforms other methods in terms of accuracy, efficiency, and robustness.