Development and validation of a smart HVAC control system for multi-occupant offices by using occupants’ physiological signals from wristband

Deng, Zhipeng; Chen, Qingyan

doi:10.1016/j.enbuild.2020.109872

Cited by 65 publications

(21 citation statements)

References 49 publications

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“…To improve asset management, it is also feasible to interpret printed and handwritten text, read picture information, and build useful metadata for smart image catalogs (Czerniawski and Leite, 2018). Deep learning can automatically separate Red Green Blue-Depth (RGB-D) photos into different construction parts (Deng and Chen, 2020). A shared convolutional neural network (CNN) is used for feature extraction from pictures in a vision and learningbased indoor localization framework (Wei and Akinci, 2019).…”

Section: Classification Of Imagesmentioning

confidence: 99%

A review of the Digital Twin technology for fault detection in buildings

Hosamo

Nielsen

Alnmr

et al. 2022

Front. Built Environ.

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This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology is one of the most intriguing newly discovered technologies rapidly evolving; however, some problems still need to be addressed. First, using Digital Twins to detect building faults to prevent future failures and cutting overall costs by improving building maintenance is still ambiguous. Second, how Digital Twin technology may be applied to discover inefficiencies inside the building to optimize energy usage is not well defined. To address these issues, we reviewed 326 documents related to Digital Twin, BIM, and fault detection in civil engineering. Then out of the 326 documents, we reviewed 115 documents related to Digital Twin for fault detection in detail. This study used a qualitative assessment to uncover Digital Twin technology’s full fault detection capabilities. Our research concludes that Digital Twins need more development in areas such as scanner hardware and software, detection and prediction algorithms, modeling, and twinning programs before they will be convincing enough for fault detection and prediction. In addition, more building owners, architects, and engineers need substantial financial incentives to invest in condition monitoring before many of the strategies discussed in the reviewed papers will be used in the construction industry. For future investigation, more research needs to be devoted to exploring how machine learning may be integrated with other Digital Twin components to develop new fault detection methods.

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Section: Classification Of Imagesmentioning

confidence: 99%

A review of the Digital Twin technology for fault detection in buildings

Hosamo

Nielsen

Alnmr

et al. 2022

Front. Built Environ.

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“…Today, "smart" house systems have occupied one of the key niches in development in the field of intelligent information technology [1][2][3][4][5][6]. Such systems continuously generate a stream of data, that is used to solve a number of important tasks, namely, the automation of home appliances control [7,8], providing comfortable living conditions [9,10], reducing energy consumption [11,12], etc. Similar systems can also be used to monitor the elderly, children and pets [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

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In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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“…Mohammad et al [ 17 ] used a wristband to obtain real-time metabolic rates and enhance PMV calculation. Deng and Chen [ 18 ] developed an HVAC control system based on the physiological features measured by a wristband, i.e., skin temperature, skin humidity, and heart rate. The result shows that less than 5% of occupants feel discomfort under the system.…”

Section: Introductionmentioning

confidence: 99%

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

Pao

Liang

et al. 2022

IJERPH

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Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

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Development and validation of a smart HVAC control system for multi-occupant offices by using occupants’ physiological signals from wristband

Cited by 65 publications

References 49 publications

A review of the Digital Twin technology for fault detection in buildings

A review of the Digital Twin technology for fault detection in buildings

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

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