Optimizing Energy Consumption in Smart Homes Using Ml Techniques

Because of growing demand and rising electricity prices, energy consumption in smart homes is a serious concern. Conventional energy management systems frequently lack flexibility, which results in wasteful energy use. In order to improve energy efficiency in smart homes, this study suggests a machine learning-based optimization strategy that combines Reinforcement Learning (RL), Support Vector Machine (SVM), and Genetic Algorithm (GA). Using past consumption trends and environmental variables, SVM is used to forecast energy demand. GA minimizes energy consumption while preserving user comfort to optimize appliance scheduling. Through constant interaction with the environment, RL dynamically learns and modifies energy consumption techniques, allowing for real-time optimization. The suggested architecture creates an adaptive energy management system by utilizing machine learning techniques and IoT-based data collection. According to experimental results, energy usage and costs can be significantly reduced while maintaining user convenience. This study advances the creation of data-driven, intelligent energy management systems for environmentally friendly smart homes.