Multi-Dimensional IoT-Based Energy Management Approach for Smart Homes: A Unified Model for Comfort and Energy Efficiency

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic constraints is applied across a 24-hour horizon with 15-minute intervals, supported by an integrated battery storage system to balance peak demands. The model also maximizes the use of harvested energy to further enhance efficiency. Experimental results show that the proposed dynamic framework outperforms standard control methods by achieving substantial energy savings, maintaining stable indoor conditions, and effectively using renewable energy storage. Compared and validated with baseline energy consumption scenarios, this model represents a significant advancement for smart home energy management, offering a scalable and sustainable solution that adapts seamlessly to various operational and environmental conditions.