Forecasting Energy Demand for Electric Vehicles Using Machine Learning Techniques

The accelerating adoption of electric vehicles complicates the prediction of consumers’ charging times. The uncertainty surrounding these patterns might make it more crucial to estimate future electricity requirements. Target here: devise a machine-learning method that forecasts energy needs in the near future because electric vehicles. The work in the study uses the available information to assess the past behaviour of how much power EV’S drew while charging. The analysis also includes trends in electric vehicle purchases. It also includes weather related elements. Forecast accuracy is enhanced by features derived from previous demand figures, moving averages, and timing patterns. A Random Forest Regressor is paired with a Long Short-Term Memory, an RNN. Based on the experimental result, the XGBoost model surpassed all others with a larger coefficient of determination and greater prediction error. The energy required for electric vehicles (EVs) can be accurately predicted for as much as 14 days ahead. The framework can enable data-driven decision-making for sustainable electric mobility of developing energy competence and EV infrastructure. Index Terms—Electric Vehicles, Energy Demand Forecasting, Machine Learning, XGBoost, Random Forest, LSTM, Smart Grid, Time Series Prediction.