The field of building energy management is experiencing a significant shift towards more personalized and adaptive systems. Researchers are exploring the potential of Human-in-the-Loop (HITL) Artificial Intelligence and reinforcement learning to optimize Heating, Ventilation, and Air Conditioning (HVAC) systems, taking into account real-time user feedback and fluctuating electricity prices. This approach enables better balancing of individual comfort preferences with economic and environmental goals. Additionally, there is a growing focus on developing more inclusive and accessible design solutions, leveraging automated optimization processes and thermal detection technologies to improve the experiences of users with disabilities. Noteworthy papers in this area include: The Human-in-the-Loop AI for HVAC Management paper, which proposes a novel framework for optimizing HVAC performance. The Thermal Detection of People with Mobility Restrictions paper, which introduces a fully automated thermal detector-based traffic light system for enhancing barrier-free intersection.
Optimizing Building Energy Efficiency and Accessibility
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Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control
A computer vision-based model for occupancy detection using low-resolution thermal images
Human-in-the-Loop Optimization for Inclusive Design: Balancing Automation and Designer Expertise
Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections