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1. Thermal Comfort: Radiant Systems - A Review of Experimentalbased thermal comfort research in Radiation systems

   JAWADWALA, HUZEFA ; TIAN, ROBERT ; YIN, HONGXI ; QU, MING ; LIU, XIAOLI ; HOLMES, NIALL ; KOHANOFF, JORGE ; JANI, RUCHITA ( March 2021 , 108th ACSA Annual Meeting Proceeding)

   null (Ed.)

   Buildings use 40% of the global energy consumption and emit 30% of the CO2 emissions [1]. Of the total building energy, 30-40% are for building heating and cooling systems, which regulate the indoor thermal environment and provide thermal comfort to occupants. In the United States, most buildings use forced air technology to deliver heating/cooling to the targeted thermal zones. However, this system may cause complaints about thermal comfort from inhabitants due to excessive draft movement, inhomogeneous conditioning, and difficulty in accurately controlling the temperature for a system serving multiple rooms. Therefore, researchers have suggested using a radiant heating and cooling system as a better alternative to all-air systems to address these issues. Radiant systems supply heating or cooling directly to the building space using radiation released by the heated or cooled building enclosure via the embedded heating or cooling tubes. In the cooling season, the radiant system often works with a separated dehumidifier to meet space latent and sensible cooling load (called separate sensible and latent cooling system SSLC). The SSLC has shown higher efficiency than forced air systems. However, it is unsure whether the radiant heating and cooling system can provide better thermal comfort to occupants. Moreover, the evaluation method for thermal comfort in the current standard is suitable for forced air systems. Therefore, a new method shall be developed to evaluate the radiation system’s thermal comfort. In this paper, we review the experiment-based studies on the thermal comfort of radiant systems. According to the experimental studies regarding thermal comfort and radiant systems, the key findings are concluded to help guide the evaluation of thermal comfort for radiant systems. 

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2. 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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3. 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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4. Thermal and RGB-D Sensor Fusion for Non-Intrusive Human Thermal Comfort Assessment

   Li, Da ; Menassa, Carol. C ; and Kamat, Vineet R. ( January 2019 , CIB World Building Congress 2019, Hong Kong)

   It is well established that thermal comfort is an influential factor in human health and wellbeing. Uncomfortable thermal environments can reduce occupants’ comfort and productivity, and cause symptoms of sick building syndrome. To harness the built environment as a medium to support human health, well-being, and engagement, it is significantly important to understand occupants’ thermal comfort in real time. To this end, this study proposes a non-intrusive method to collect occupants’ facial skin temperature and interpret their thermal comfort conditions by fusing the thermal and RGB-D images collected from multiple low-cost thermographic and Kinect sensors. This study distinguishes from existing methods of thermal comfort assessment in three ways: 1) it is a truly non-intrusive data collection approach which has a minimal interruption or participation of building occupants; 2) the proposed approach can simultaneously identify and interpret multiple occupants’ thermal comfort; 3) it uses low-cost thermographic and RGB-D cameras which can be rapidly deployed and reconfigured to adapt to various settings. This approach was experimentally evaluated in a transient heating environment (room temperature increased from 23 to 27 °C) to verify its applicability in real operational built environments. In total, all 6 subjects observed moderate to strong positive correlations between the ambient room temperature and subjects’ facial skin temperature collected using the proposed approach. Additionally, all 6 subjects have voted different thermal sensations at the beginning (the first 5 minutes) and at the end (the last 5 minutes) of the heating experiment, which can be reflected by the significant differences in the mean skin temperature of these two periods (p < .001). Results of this pilot study demonstrate the feasibility of applying the proposed non-intrusive approach to real multi-occupancy environments to dynamically interpret occupants’ thermal comfort and optimize the operation of building heating, ventilation and air conditioning (HVAC) systems. 

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5. Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography

   Li, Da ; Menassa, Carol. C ; and Kamat, Vineet R. ( January 2018 , Energy and buildings)

   Understanding occupants’ thermal sensation and comfort is essential to defining the operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings. Due to the continuous impact of human and environmental factors, occupants’ thermal sensation and comfort level can change over time. Thus, to dynamically control the environment, thermal comfort should be monitored in real time. This paper presents a novel non-intrusive infrared thermography framework to estimate an occupant’s thermal comfort level by measuring skin temperature collected from different facial regions using low- cost thermal cameras. Unlike existing methods that rely on placing sensors directly on humans for skin temperature measurement, the proposed framework is able to detect the presence of occupants, extract facial regions, measure skin temperature features, and interpret thermal comfort conditions with minimal interruption of the building occupants. The method is validated by collecting thermal comfort data from a total of twelve subjects under cooling, heating and steady-state experiments. The results demonstrate that ears, nose and cheeks are most indicative of thermal comfort and the proposed framework can be used to assess occupants’ thermal comfort with an average accuracy of 85%. 

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