ML Forecasting for Energy Demand Response

Overview

Accurate demand forecasting and effective demand response programs are crucial for grid stability and energy efficiency. This talk examines how advanced machine learning improves demand predictions while enabling sophisticated demand response strategies. We explore neural networks for capturing temporal consumption patterns, integrating weather and socioeconomic data in forecasting models, and computer vision for regional demand prediction. The session covers reinforcement learning for optimizing demand response incentives, federated learning for privacy-preserving forecasting, and real-world applications including smart home systems and industrial demand response programs.