Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency

• URL: http://arxiv.org/abs/2505.05796v1
• Date: Fri, 09 May 2025 05:23:37 GMT
• Title: Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency
• Authors: Xinyu Liang, Frits de Nijs, Buser Say, Hao Wang,
• Abstract summary: We propose a Human-in-the-Loop (HITL) Artificial Intelligence framework that optimize HVAC performance by incorporating real-time user feedback and responding to electricity prices.<n>Unlike conventional systems that require predefined information about occupancy or comfort levels, our approach learns and adapts based on ongoing user input.<n>By integrating the occupancy prediction model with reinforcement learning, the system improves operational efficiency and reduces energy costs in line with electricity market dynamics.
• Score: 10.01352946835079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heating, Ventilation, and Air Conditioning (HVAC) systems account for approximately 38% of building energy consumption globally, making them one of the most energy-intensive services. The increasing emphasis on energy efficiency and sustainability, combined with the need for enhanced occupant comfort, presents a significant challenge for traditional HVAC systems. These systems often fail to dynamically adjust to real-time changes in electricity market rates or individual comfort preferences, leading to increased energy costs and reduced comfort. In response, we propose a Human-in-the-Loop (HITL) Artificial Intelligence framework that optimizes HVAC performance by incorporating real-time user feedback and responding to fluctuating electricity prices. Unlike conventional systems that require predefined information about occupancy or comfort levels, our approach learns and adapts based on ongoing user input. By integrating the occupancy prediction model with reinforcement learning, the system improves operational efficiency and reduces energy costs in line with electricity market dynamics, thereby contributing to demand response initiatives. Through simulations, we demonstrate that our method achieves significant cost reductions compared to baseline approaches while maintaining or enhancing occupant comfort. This feedback-driven approach ensures personalized comfort control without the need for predefined settings, offering a scalable solution that balances individual preferences with economic and environmental goals.

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