Lightweight machine learning framework using temporal features for electric vehicle demand response forecasting on edge devices.

The popularity of Electric Vehicles (EVs) poses particular challenges for demand-side energy management, especially in low-computation scenarios. This research builds a lightweight machine learning (ML) model to forecast EV consumption load and to maximise Demand Response (DR) strategies using real-world data. The Kaggle dataset used in this research consists of time-series EV charging data, which is preprocessed, down sampled, and augmented with time-based features, including hour, weekday, and month. Seven DR strategies, which are Peak Clipping, Valley Filling, Load Shifting, Load Levelling, Strategic Load Growth, Strategic Conservation, and Flexible Load Shape, are implemented on estimated load profiles. The model experiments consist of five ML models: Linear Regression (LR), Support Vector Regression (SVR), k-Nearest Neighbours (kNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and their results for both prediction and computation. Such models are selected since they are suitable for systems that lack resources. To serve all DR cases, the measurement metrics used are MAE, RMSE, and R