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

2025-05-09

AI Summary

Traditional HVAC systems struggle to simultaneously accommodate real-time electricity price fluctuations and individual thermal comfort preferences, resulting in suboptimal energy efficiency and low occupant satisfaction. This paper proposes a human-in-the-loop AI framework that employs online proximal policy optimization (PPO) to directly integrate real-time occupant comfort feedback and dynamic pricing signals within a closed-loop control architecture—without requiring predefined occupancy or thermal comfort models. The approach enables joint optimization of personalized thermal regulation and grid-responsive demand management. Key innovations include: (i) the first model-free, feedback-driven online learning mechanism for HVAC control; and (ii) inherent scalability to large-scale building portfolios. Simulation results demonstrate an 18.7% reduction in energy cost and a 23% improvement in predicted mean vote (PMV)-based comfort relative to benchmark methods.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Optimizing HVAC systems for energy efficiency and comfort
• Adapting to real-time electricity prices and user preferences
• Reducing energy costs while maintaining occupant comfort

Innovation

• Human-in-the-Loop AI for HVAC optimization
• Reinforcement learning with occupancy prediction
• Real-time feedback for dynamic comfort control