iTCM: Toward Learning-Based Thermal Comfort Modeling via Pervasive Sensing for Smart Buildings

Hu, Weizheng; Wen, Yonggang; Guan, Kyle; Jin, Guishan; Tseng, King Jet

doi:10.1109/jiot.2018.2861831

Cited by 48 publications

(39 citation statements)

References 36 publications

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“…Ambience control of a museum has been performed in [202] where the authors use deep learning algorithms to predict the CO 2 and humidity levels for the care of exhibits. Comfort aware energy management has been performed in [203] where the authors use a CNN to regulate thermal comfort in a building using various physical quantities. It can be noted that all of these mentioned systems have been deployed in the cloud, this is due to the nature of the application.…”

Section: Smart Industrymentioning

confidence: 99%

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IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Industrymentioning

confidence: 99%

“…Heterogeneous (environmental data such as CO 2 , Humidity, Air velocity) CNN [203] Regression-Comfort level…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…In these papers, the authors made use of different types of sensors, including motion sensors [18,25,28,[141][142][143][144][145]; temperature sensors [28,40,73,143,144]; wireless sensor networks [21,40,141,145]; door sensors [25,143]; smartphone inertial sensors [146] and a smartphone application [36]; cameras [18]; a two-dimensional acoustic array [27]; daily activity recognition sensors [28]; actuators [143]; tactile sensors, power meters, and microphones in the ceiling [144]; non-wearable sensors [147]; unobtrusive sensors [9]; environmental sensors [73,142]; weather sensors [12]; WiFi-enabled sensors for food nutrition quantification [36]; and binary sensors [148]. In the scientific papers selected and summarized in Table S16, the reasons for using Deep Learning techniques integrated with sensor devices in smart buildings were mainly related to human activity recognition [9,18,25,27,28,73,142,143,…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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“…Building indoor comfort conditions depend upon indoor relative humidity percentage (RH%) and temperature which must be finely adjusted to optimize building energy consumption [1], [2]. Many models and indices have been researched and developed so far for measuring accurate indoor thermal comforts such as predictive mean value (PMV) [3], index of thermal stress [4], predicted percentage dissatisfied…”

Section: Introductionmentioning

confidence: 99%

Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System

et al. 2020

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Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from −5 to +5. This requires recruitment of subjects and matching for gender, age etc. In this study, we have tried to predict the thermal comfort of occupants by observing their real behavior inside the test room fitted with a novel thermoelectric air duct (TE-AD) cooling system rather than a conventional air conditioning system. Firstly, real experimental data were collected for more than two months from the test room equipped with the TE-AD cooling system operated at an input power supply of 6 A and 5 V. After that, the ANN model was developed based on the Levenberg-Marquardt algorithm by taking experimental parameters such as air temperature, relative humidity, globe temperature, wind speed, metabolic rate, and clothing value as model input. The ANN model is optimized by developing different models with different data points as a starting input in the training and validation process. The neuron optimization has been carried out in these models to minimize the mean square error (MSE) for the ANN model. The result shows that among the three models M1, M2, and M3, the optimum predictive mean value (PMV) was obtained from M1 at 10 neurons with MSE of 0.07956, while for predicted percentage dissatisfied (PPD), M3 gives optimum accuracy at 10 neurons with MSE value of 5.1789. The ANN model is then generalized to predict thermal comfort for one week and then for one month. Finally, all the model results were validated with the experimental data.INDEX TERMS Artificial neural network, indoor temperature, relative humidity, thermal comfort, thermoelectric air duct.

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iTCM: Toward Learning-Based Thermal Comfort Modeling via Pervasive Sensing for Smart Buildings

Cited by 48 publications

References 36 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System

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