AI-Powered Adaptive Energy Optimization Using Dynamic Thermal Flow Simulation in Smart Buildings
Year: 2025
Type: article
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.
DOI: 10.14419/tz71rz37
Authors: Dr. Nidhi Mishra, Adil Raja, Bhumika Bansal
Institution: Kalinga University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415764231
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