Energy Optimization in Smart Grids Using Machine Learning and IoT

Abstract

Machine learning is transforming smart grid management by optimizing energy distribution, forecasting demand, and reducing energy waste. This paper explores ML algorithms such as reinforcement learning, regression models, and neural networks applied to IoT-enabled energy grids. It discusses challenges like data latency, integration complexity, and cybersecurity risks. Case studies demonstrate successful implementations of ML-driven smart grid solutions, highlighting improvements in energy efficiency, cost reduction, and sustainability.