AI-Based Load Forecasting for Demand Response

Accurate load forecasting is essential for energy system demand response, energy distribution, and energy waste reduction. This work investigates artificial intelligence-based energy load estimation methods, focussing on Gradient Boosting Machines (GBM) to improve forecast accuracy. The study will focus on GBM use. The research project begins with extensive data preprocessing. This solution includes imputation for missing data and data inclusion to fix dataset imbalances. To improve model performance, autoencoder-based feature selection reduces data dimensionality while keeping crucial information. We also use a correlation matrix to find and remove duplicate characteristics. After that, the Gradient Boosting Machine classifier is used for regression jobs to manage nonlinear relationships and reduce prediction mistakes. This improves forecast accuracy. The studies show that the suggested strategy enhances load forecasting accuracy compared to standard models. This study shows that AI-driven methods can improve demand responsiveness. This can help smart grids and telecoms manage energy more efficiently and sustainably.