Smart Energy Management System for Smart Homes Using Deep Learning

Abstract

The increasing integration of smart home technologies and IoT-enabled appliances has intensified the need for intelligent energy management systems capable of reducing energy consumption while maintaining user comfort. Traditional energy management approaches often rely on static rules or basic optimization techniques, which are insufficient for handling dynamic household energy usage patterns. This study proposes a smart energy management system for smart homes using deep learning techniques to predict, optimize, and control energy consumption in real time. The methodology involves collecting multivariate data from smart meters, environmental sensors, and appliance-level monitoring systems, followed by preprocessing steps including normalization, feature extraction, and time-series segmentation. A hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and attention mechanisms is developed to forecast short-term and long-term energy demand. Based on prediction outputs, an optimization module is implemented to schedule appliance usage and reduce peak load demand while ensuring occupant comfort. The system is evaluated using performance metrics such as prediction accuracy, energy savings percentage, mean absolute error, and system response time. The results demonstrate that the proposed model achieves high accuracy in energy consumption forecasting and significantly reduces overall household energy usage compared to conventional rule-based and statistical models. Additionally, the system effectively shifts energy consumption from peak to off-peak hours, contributing to load balancing and improved grid stability. The study concludes that deep learning-based smart energy management systems offer a scalable and adaptive solution for efficient residential energy utilization.