scholarly journals Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 720 ◽  
Author(s):  
Youssef ◽  
Caballero ◽  
Aerts
Keyword(s):  
Metabolic Rate ◽  
Thermal Comfort ◽  
Thermal State ◽  
Thermal Sensation ◽  
Streaming Data ◽  
Classification Model ◽  
Percentage Error ◽  
Support Vector ◽  
Activity Levels ◽  
Model Based

Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. Recent advances in wearable sensing technologies and their generated streaming data are providing a unique opportunity to understand the user’s behaviour and to predict future needs. Estimation of thermal comfort is a challenging task given the subjectivity of human perception; this subjectivity is reflected in the statistical nature of comfort models, as well as the plethora of comfort models available. Additionally, such models are using not-easily or invasively measured variables (e.g., core temperatures and metabolic rate), which are often not practical and undesirable measurements. The main goal of this paper was to develop dynamic model-based monitoring system of the occupant’s thermal state and their thermoregulation responses under two different activity levels. In total, 25 participants were subjected to three different environmental temperatures at two different activity levels. The results have shown that a reduced-ordered (second-order) multi-inputs-single-output discrete-time transfer function (MISO-DTF), including three input variables (wearables), namely, aural temperature, heart rate, and average skin heat-flux, is best to estimate the individual’s metabolic rate (non-wearable) with a mean absolute percentage error of 8.7%. A general classification model based on a least squares support vector machine (LS-SVM) technique is developed to predict the individual’s thermal sensation. For a seven-class classification problem, the results have shown that the overall model accuracy of the developed classifier is 76% with an F1-score value of 84%. The developed LS-SVM classification model for prediction of occupant’s thermal sensation can be integrated in the heating, ventilation and air conditioning (HVAC) system to provide an occupant thermal state-based climate controller. In this paper, we introduced an adaptive occupant-based HVAC predictive controller using the developed LS-SVM predictive classification model.

scholarly journals Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

2019 ◽  
Vol 9 (16) ◽  
pp. 3303 ◽  
Author(s):  
Ali Youssef ◽  
Ahmed Youssef Ali Amer ◽  
Nicolás Caballero ◽  
Jean-Marie Aerts
Keyword(s):  
Thermal Comfort ◽  
Thermal State ◽  
Thermal Sensation ◽  
Wearable Sensors ◽  
Building Design ◽  
Streaming Data ◽  
Classification Model ◽  
Support Vector ◽  
Indoor Climate ◽  
Climate Control

Thermal comfort and sensation are important aspects of building design and indoor climate control, as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide opportunity for personalized climate control and thermal comfort enhancement. Recent advances in wearable technologies contributed to new possibilities in controlling and monitoring health conditions and human wellbeing in daily life. The generated streaming data generated from wearable sensors are providing a unique opportunity to develop a real-time monitor of an individual’s thermal state. The main goal of this work is to introduce a personalized adaptive model to predict individual’s thermal sensation based on non-intrusive and easily measured variables, which could be obtained from already available wearable sensors. In this paper, a personalized classification model for individual thermal sensation with a reduced-dimension input-space, including 12 features extracted from easily measured variables, which are obtained from wearable sensors, was developed using least-squares support vector machine algorithm. The developed classification model predicted the individual’s thermal sensation with an overall average accuracy of 86%. Additionally, we introduced the main framework of streaming algorithm for personalized classification model to predict an individual’s thermal sensation based on streaming data obtained from wearable sensors.

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

2016 ◽  
Vol 25 (8) ◽  
pp. 1248-1258 ◽  
Author(s):  
Fayçal Megri ◽  
Ahmed Cherif Megri ◽  
Riadh Djabri
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Support Vector Regression ◽  
Thermal Sensation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Statistical Regression ◽  
Swarm Optimization ◽  
Comfort Indices

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors

scholarly journals Investigating Thermal Comfort for the Classroom Environment using IoT

2018 ◽  
Vol 9 (1) ◽  
pp. 157 ◽  
Author(s):  
Nurshahrily Idura Ramli ◽  
Mohd Izani Mohamed Rawi ◽  
Ahmad Zahid Hijazi ◽  
Abdullah Hayyan Kunji Mohammed
Keyword(s):  
Environmental Factors ◽  
Metabolic Rate ◽  
Thermal Comfort ◽  
Classroom Environment ◽  
Thermal Sensation ◽  
Influencing Factor ◽  
Physiological Factors ◽  
Clothing Insulation ◽  
Air Movement ◽  
Radiant Temperature

<p>In this modern century where fine comfort is a necessity especially in buildings and occupied space, the study to satisfy one aspect of human comfort is a must. This study encompasses of exploring the physiological and environmental factors in achieving thermal comfort which specifically considering the clothing insulation and metabolic rate of students as well as the deployment of dry-bulb temperature, mean radiant temperature, humidity, and air movement in order to obtain the level of comfort students are experiencing in class. The level of comfort are detected by using ASHRAE 55 to determine the average thermal sensation response through the Predicted Mean Vote (PMV) value. An android application were developed to read input of recognizing clothing level (thickness of clothing) and capturing metabolic rate to cater the inputs for physiological factors, while radiant temperature, humidity and air movement are captured through static sensors set up in the classroom space. This paper analyses both the physiological and environmental factors in affecting students in class and further determine their comfort levels that is a major influencing factor of focus in learning. Through cross referencing collected data from IoT enabled nodes, it is found that both physiological and environmental factors, and the combination of them greatly influence in getting the most comfortable state with PMV value of 0.</p>

Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

RSC Advances ◽  
2015 ◽  
Vol 5 (61) ◽  
pp. 49195-49203 ◽  
Author(s):  
Ting-Ting Yao ◽  
Jing-Li Cheng ◽  
Bing-Rong Xu ◽  
Min-Zhe Zhang ◽  
Yong-Zhou Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Rational Design ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classification ◽  
Tetronic Acid ◽  
Model Based

A novel SVM classification model was constructed and applied in the development of novel tetronic acid derivatives as potent insecticidal and acaricidal agents.

An accurate traffic classification model based on support vector machines

2017 ◽  
Vol 27 (1) ◽  
pp. e1962 ◽  
Author(s):  
Jie Cao ◽  
Zhiyi Fang ◽  
Guannan Qu ◽  
Hongyu Sun ◽  
Dan Zhang
Keyword(s):  
Support Vector Machines ◽  
Classification Model ◽  
Support Vector ◽  
Traffic Classification ◽  
Model Based ◽  
Vector Machines

scholarly journals A Comprehensive Support Vector Machine Binary hERG Classification Model Based on Extensive but Biased End Point hERG Data Sets

2011 ◽  
Vol 24 (6) ◽  
pp. 934-949 ◽  
Author(s):  
Meng-yu Shen ◽  
Bo-Han Su ◽  
Emilio Xavier Esposito ◽  
Anton J. Hopfinger ◽  
Yufeng J. Tseng
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Data Sets ◽  
Model Based ◽  
End Point
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