Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions

Cosma, Andrei Claudiu; Simha, Rahul

doi:10.1016/j.buildenv.2018.11.017

Cited by 102 publications

(46 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A personal comfort model using only control behavior of a personal chair system can generate a prediction AUC of 69% compared to approximately 53% (almost random) for the PMV and adaptive model [26]. Along with the behavior-tracking, physiological signals, such as skin temperature [27][28][29][30][31], heart rate variability [32], electroencephalogram (EEG) [33], skin conductance [34], and accelerometry [35], show a strong relationship with human thermal sensation and comfort. Sim et al [36] developed personal thermal sensation models based on wrist skin temperature measured by wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

200

View full text Add to dashboard Cite

A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24% /78% /0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21% /71% /0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35% /76% /0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.

show abstract

Section: Introductionmentioning

confidence: 99%

Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

200

View full text Add to dashboard Cite

show abstract

“…The sensing platform followed three principles, which are low cost (USD 300), small form-factor device and real-time capabilities. Based on the methods, machine learning method was used to do prediction and analysis (Cosma and Simha, 2019). Infrared thermal camera network, composed by low-cost thermal cameras and RGB-D sensors (Kinect), was tried to overcome influences of occupants' postures and movements .…”

Section: Infrared Camera Technologymentioning

confidence: 99%

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Yang

Hou

et al. 2020

Energy and Buildings

127

View full text Add to dashboard Cite

show abstract

“…Predicting occupants' thermal comfort by ML algorithms is another key methodology related to artificial intelligence algorithms. Cosma and Simha introduced a non-invasive approach for automatic prediction of personal thermal comfort for real-time feedback with ML [14]. Chaudhuri et al proposed an indoor-climate control framework to decrease the disparity between energy-efficiency and indoor thermal-comfort in buildings, which comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm [15].…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Zhao

Genovese

2020

Future Internet

View full text Add to dashboard Cite

With the improvement of technologies, people's demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.Future Internet 2020, 12, 30 2 of 18 prediction, and the error is relatively large. In recent years, the development trends of computer science, applied mathematics, statistics, and semiconductor hardware have brought iterative progress in artificial intelligence technology. This makes the simulation, analysis, prediction. Literature ReviewBig data mainly refers to data management on a certain scale. Due to the increase of the data volume, speed, and type, traditional methods cannot be used to proceed [1]. The artificial intelligence technology represented by machine learning and deep learning algorithms has a great dependence on datasets. Only when the original data is large enough can the trained algorithm be more accurate. Therefore, before applying the model, it is necessary to obtain enough data for repeated training.In the traditional building with related planning and landscape fields, big data was firstly applied to Geographic Information System (GIS), including a variety of spatial information such as land conditions, terrain, and climate. As for the single building, its automated development is mainly reflected in the field of intelligent building, with small but higher accuracy. The complexity of intelligent building systems is high. Meanwhile, the physical structure, indoor environment, and operating systems of the entire building are monitored and analyzed. It can develop the building in the direction of intelligence. With the help of building big data system, it can analyze the use of the building in real-time, integrate the related information of the surroundings, and let the building make optimal adjustments according to the actual situation [2]. In the process of optimal adjustment, IoT sensors and controllers are usually targeted in the building. The organic combination of physical devices and computer algorithms pla...

show abstract

Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions

Cited by 102 publications

References 21 publications

Personal thermal comfort models with wearable sensors

Personal thermal comfort models with wearable sensors

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Contact Info

Product

Resources

About