AI-Powered Adaptive Energy Optimization Using Dynamic‎Thermal Flow Simulation in Smart Buildings

Abstract

The increasing demands of smart buildings in terms of energy efficiency, dynamic environmental conditions, varied occupancy, and shifting ‎energy demand demand innovative solutions. The contribution of this research is to propose a Powered Adaptive Energy Optimization ‎System (AEOS) based on dynamic thermal flow simulation and AI-powered predictive analytics, providing an intelligent building with ‎advanced energy-efficient technologies that can enhance energy efficiency. We run our technologies using CFD, which simulates real-time ‎airflow, heat distribution, and energy patterns indoors. The AEOS forecasts optimal HVAC change using RL models with occupancy ‎behavior, outside weather information, and inside thermal conditions. The sensor network is collected from a periodically IoT-enabled ‎sensor network and then processed using an adaptive control mechanism to control heating, ventilation, and air conditioning dynamically.‎ Furthermore, a real-time load-balancing scheme that minimizes energy wastage is proposed for occupant comfort, utilizing the Deep Q ‎Learning Network (DQN) and Genetic Algorithm (GA). The reduction of energy consumption by 30% is experimentally evaluated in the ‎performance of AEOS, which outperforms standard traditional static energy models. That is because the adaptive system will increase its ‎long-term efficiency with the feedback loops. The proposed solution can be an innovative, viable way to reduce smart building management, ‎offering scalability, sustainability, and regional and global contributions to energy conservation, indoor comfort, and environmental stability.