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1. Home Energy Management with Clothing Integrated Thermal Comfort and EV SOC Concern

   https://doi.org/10.1109/PESGM48719.2022.9917003  

   Liu, Xuebo; Wu, Yingying; Zhang, Hang; Liu, Bo; Edmonds, Lawryn; Wu, Hongyu (July 2022, 2022 IEEE Power & Energy Society General Meeting (PESGM))

   This paper proposes a home energy management system (HEMS) while considering the residential occupant’s clothing integrated thermal comfort and electrical vehicles (EV) state-of-charge (SOC) concern. An adaptive dynamic program- ming (ADP) based HEMS model is proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), the donning/doffing decisions for the clothing conditions and charging/discharging of EV while taking into account the uncertainties in outside temperature and EV arrival SOC. We use model predictive control (MPC) to simulate a multi-day energy management of a residential house equipped with the proposed HEMS. The proposed HEMS is compared with a baseline case without the HEMS. The simulation results show that a 47.5% of energy cost saving can be achieved by the proposed HEMS while maintaining satisfactory occupant thermal comfort and negligible EV SOC concerns. 

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2. 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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3. Enhancing HVAC energy management through multi-zone occupant-centric approach: A multi-agent deep reinforcement learning solution

   https://doi.org/10.1016/j.enbuild.2023.113770  

   Liu, Xuebo; Wu, Yingying; Wu, Hongyu (January 2024, Energy and Buildings)

   Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants. 

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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. 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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