Electric vehicle charging demand forecasting at charging stations under climate influence for electricity dispatching

Abstract

As the prevalence of electric vehicles (EVs) continues to surge, the precise forecasting of charging demands at individual charging stations becomes imperative for effective power distribution management. However, the charging demand of EVs is often related to various factors and exhibits strong randomness. This paper aims to explore the impact of climatic factors on the charging demand of electric vehicles at charging stations, and to study a prediction model based on the attention mechanism of LSTM for predicting the load of charging stations, providing important guidance for the scheduling of electric vehicles. By analysing the load data of a single charging station under different climatic conditions, the paper finds that climatic factors such as the highest temperature of the day, the lowest temperature, and the type of weather significantly affect the charging demand of electric vehicles. Utilizing the aforementioned characteristics, this paper studies a climate feature-guided charging demand prediction model for a single charging station, which adopts the LSTM architecture and introduces the attention mechanism, incorporating the above-mentioned important climatic features, and is ultimately able to accurately predict the future charging demand of the charging station. The experimental results show that, compared to other time series forecasting models, this model has significantly improved performance on the dataset tests, with its accuracy ratio (AR) indicator and qualified rate indicator both exceeding 0.85 and 0.95, respectively. This study not only offers a new perspective and method for predicting the demand for electric vehicle charging but also provides support for the development of electric vehicle scheduling.