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1. A Low-Cost and Scalable Personalized Thermal Comfort Estimation System in Indoor Environments

   https://doi.org/10.1145/3458648.3460006  

   Wei, Peter; Liu, Yanchen; Kang, Hengjiu; Yang, Chenye; Jiang, Xiaofan (May 2021, Proceedings of the First International Workshop on Cyber-Physical-Human System Design and Implementation)

   null (Ed.)

   In commercial buildings, occupant thermal comfort is a key factor that must be optimized to provide a comfortable and productive work environment. However, current methods largely estimate thermal comfort based on preset models which do not incorporate real-time measurements or individual thermal preferences. In this work, we present a scalable system for estimating personalized thermal comfort using low-cost thermal camera based sensor nodes. This system extracts non-intrusive thermal measurements, is robust to different perspectives and environments, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by 64% over baseline methods. 

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2. Low-Cost, Perspective Invariant and Personalized Thermal Comfort Estimation: Poster Abstract

   https://doi.org/10.1145/3412382.3458781  

   Wei, Peter; Liu, Yanchen; Jiang, Xiaofan (May 2021, Proceedings of the 20th International Conference on Information Processing in Sensor Networks)

   null (Ed.)

   In this poster abstract, we present a thermal comfort estimation system using low-cost thermal camera based sensor nodes. This system extracts perspective invariant, non-intrusive thermal measurements, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by up to 64% over baseline methods. 

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3. Doppler Radar Occupancy Sensor Assessment of Thermal Adaptation

   https://doi.org/10.1109/APMC57107.2023.10439672  

   Setthapittayakul, Wannasa; Sameera, Jannatun Noor; Lubecke, Victor M; Boric-Lubecke, Olga (December 2023, IEEE)

   This study examines the use of a Doppler radar occupancy sensor to assess thermal adaptation. Current heating, ventilation, and air-conditioning (HVAC) systems are important for the productivity of employees and the healing of patients; however, their control systems are typically limited to a narrow temperature range, which is usually not comfortable for most occupants. Occupant vital signs can be used to assess thermal comfort, due to the role cardiovascular regulation plays in heat dissipation. This research aims to correlate physiological and thermal adaptation with the goal of optimizing HVAC system operation. A Doppler radar occupancy sensor was used to measure the physiological parameters of ten human subjects under two temperature conditions, and thermal comfort surveys were used to record thermal sensation. The results demonstrate that the Doppler radar occupancy sensor could not only detect heart rate changes due to a significant environmental temperature difference but also detect subtle changes in heart rate during thermal adaptation that were not captured by the surveys. 

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4. Energy Co-simulation of the Hybrid Cooling Control with Synthetic Thermal Preference Distributions

   Lu, S.; Zhiang, Z.; Hameen, E. C.; Lartigue, B.; Karaguzel, O. (January 2020, Symposium on Simulation in Architecture and Urban Design (SIMAUD 2020))

   Thermal comfort and energy efficiency are always the two most significant objectives in HVAC operations. However, for conventional HVAC systems, the pursuit of high energy efficiency may be at the expense of satisfactory thermal comfort. Therefore, even if centralized HVAC systems nowadays have higher energy efficiency than before in office buildings, most of them cannot adapt the dynamic occupant behaviors or individual thermal comfort. In order to realize high energy efficiency while still maintain satisfactory thermal environment for occupants indoors, the integrated hybrid HVAC system has been developed for years such as task-ambient conditioning system. Moreover, the occupant-based HVAC control system such as human- in-the-loop has also been investigated so that the system can be adaptive based on occupant behaviors. However, most of research related to personalized air-conditioning system only focuses on field-study with limited scale (i.e. only one office room), this paper has proposed a co- simulation model in energyplus to simulate the hybrid cooling system with synthetic thermal comfort distributions based on global comfort database I&II. An optimization framework on cooling set-point is proposed with the objective of energy performance and the constraints of thermal comfort distribution developed by unsupervised Gaussian mixture model (GMM) clustering and kernel density estimation (KDE). The co-simulation results have illustrated that with the proposed optimization algorithm and the hybrid cooling system, HVAC demand power has decreased 5.3% on average with at least 90% of occupants feeling satisfied. 

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5. Fast Human-in-the-Loop Control for HVAC Systems via Meta-Learning and Model-Based Offline Reinforcement Learning

   https://doi.org/10.1109/TSUSC.2023.3251302  

   Chen, Liangliang; Meng, Fei; Zhang, Ying (July 2023, IEEE Transactions on Sustainable Computing)

   Reinforcement learning (RL) methods can be used to develop a controller for the heating, ventilation, and air conditioning (HVAC) systems that both saves energy and ensures high occupants’ thermal comfort levels. However, the existing works typically require on-policy data to train an RL agent, and the occupants’ personalized thermal preferences are not considered, which is limited in the real-world scenarios. This paper designs a high-performance model-based offline RL algorithm for personalized HVAC systems. The proposed algorithm can quickly adapt to different occupants’ thermal preferences with a few thermal feedbacks, guaranteeing the high occupants’ personalized thermal comfort levels efficiently. First, we use a meta-supervised learning algorithm to train an occupant's thermal preference model. Then, we train an ensemble neural network to predict the thermal states of the considered zone. In addition, the obtained ensemble networks can indicate the regions in the state and action spaces covered by the offline dataset. With the personalized thermal preference model updated via meta-testing, model-based RL is used to derive the optimal HVAC controller. Since the proposed algorithm only requires offline datasets and a few online thermal feedbacks for training, it contributes to a more practical deployment of the RL algorithm to HVAC systems. We use the ASHRAE database II to verify the effectiveness and advantage of the meta-learning algorithm for modeling different occupants’ thermal preferences. Numerical simulations on the EnergyPlus environment demonstrate that the proposed algorithm can guarantee personalized thermal preferences with a slight increase of power consumption of 1.91% compared with the model-based RL algorithm with on-policy data aggregation. 

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