An Efficient XGBoost-Based Approach For Electric Load Forecasting In Smart Energy Systems

Electric load forecasting plays a crucial role in efficient power system operation and energy management. Accurate prediction of electricity demand helps in reducing operational costs and improving system reliability. However, traditional forecasting methods often fail to handle complex and non-linear patterns present in real-world data. To address this issue, this paper proposes a machine learning–based approach using Extreme Gradient Boosting (XGBoost) for electric load forecasting. \ proposed system utilizes historical load data along with important features such as time and temperature to train the model. Data preprocessing and feature selection techniques are applied to improve data quality and model performance. XGBoost, a powerful ensemble learning algorithm, is employed to capture complex relationships and enhance prediction accuracy. The model is evaluated using standard performance metrics, and the results demonstrate improved accuracy and efficiency compared to conventional methods. The proposed approach provides a reliable and scalable solution for electric load forecasting, supporting better decision-making in power system planning and management. \