Evaluating impact of different factors on electric vehicle charging demand

Abstract Electric vehicles (EVs) are emerging as major energy consumers, offering numerous environmental and operational advantages such as reduced greenhouse gas emissions and lower reliance on fossil fuels. As the adoption of EVs accelerates globally, accurate forecasting of EV charging demand becomes increasingly critical for maximizing the efficiency, reliability, and profitability of charging infrastructure. However, many existing forecasting models fall short by neglecting the complex and dynamic influence of external factors – particularly weather conditions and calendar variables – which can significantly affect usage patterns. This study presents a robust forecasting framework that integrates historical charging data with both temporal and meteorological information to comprehensively evaluate their individual and combined impacts on EV charging behavior. Leveraging long short-term memory networks – effective in modeling time-series data – we evaluate the impact of contextual features on forecasting performance. Results show that calendar information notably improves accuracy, surpassing the effect of weather data. These insights help EV station operators optimize scheduling, reduce uncertainty in day-ahead energy planning, and support sustainability and grid stability.