More Like this

1. 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. 

   more » « less
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. 

   more » « less
3. Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions

   https://doi.org/10.1016/j.buildenv.2023.110201  

   Chen, Liangliang; Ermis, Ayca; Meng, Fei; Zhang, Ying (May 2023, Building and Environment)

   The model of personalized thermal comfort can be learned via various machine learning algorithms and used to improve the individuals’ thermal comfort levels with potentially less energy consumption of HVAC systems. However, the learning of such a model typically requires a substantial number of thermal votes from the considered occupant, and the environmental conditions needed for collecting some votes may be undesired by the occupant in order to obtain a model with good generalization ability. In this paper, we propose to use a meta-learning algorithm to reduce the required number of personalized thermal votes so that a personalized thermal comfort model can be obtained with only a small number of feedback. With the learned meta-model, we derive a method based on the backpropagation of neural networks to quickly identify the best environmental and personal conditions for a specific occupant. The proposed identification algorithm has an additional advantage that the thermal comfort, indicated by the mean thermal sensation value, improves incrementally during the data collection process. We use the ASHRAE global thermal comfort database II to verify that the meta-learning algorithm can achieve an improved prediction accuracy after using 5 thermal sensation votes from an occupant to make adaptations. In addition, we show the effectiveness of the fast identification algorithm for the best personalized thermal environmental conditions with a thermal sensation generation model built from the PMV model. 

   more » « less
4. 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. 

   more » « less
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%. 

   more » « less