Evaluation of thermal comfort of the internal environment in smart home using objective and subjective factors

Vaňuš, Jan; Martínek, Radek; Bilík, Petr; Žídek, Jan; Skotnicova, Iveta

doi:10.1109/epe.2016.7521768

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…(9) μ stab = 0.0227 (M=10%) , calculation according to Eq. (10) μ opt1 = 0.0062 (M=10%) , calculation according to Eq. (11) μ opt2 = 0.0113 (M=20%) , calculation according to Eq.…”

Section: The Experimental Practical Partmentioning

confidence: 99%

“…The possibilities of developing visualization and monitoring applications using wireless components are described in detail in articles [4][5][6][7]. The actual monitoring of operational and technical variables may be used for optimizing the management of individual operational and technical functions of buildings, such as lighting [8], [9] or HVAC (Heating, Ventilation, and Air Conditioning), in order to reduce the operating costs of the building, for setting the optimal climate in individual rooms of the building [10] with respect to the requirements of the hygienic standards, or for classification [11], prediction [12] and foreseeing of building behaviour based on the variables [13] measured and processed.…”

Section: Introductionmentioning

confidence: 99%

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The design of an indirect method for the human presence monitoring in the intelligent building

Vaňuš

Machac

Martínek

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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This article describes the design and verification of the indirect method of predicting the course of CO 2 concentration (ppm) from the measured temperature variables T indoor (°C) and the relative humidity rH indoor (%) and the temperature T outdoor (°C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to

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“…(9) μ stab = 0.0227 (M=10%) , calculation according to Eq. (10) μ opt1 = 0.0062 (M=10%) , calculation according to Eq. (11) μ opt2 = 0.0113 (M=20%) , calculation according to Eq.…”

Section: The Experimental Practical Partmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The design of an indirect method for the human presence monitoring in the intelligent building

Vaňuš

Machac

Martínek

et al. 2018

Hum. Cent. Comput. Inf. Sci.

Self Cite

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“…The experimental measurements of objective parameters of the internal environment and thermal comfort evaluation were conducted in selected SH rooms R204, R203, and R104 in a wooden building of the passive standard located in the Faculty of Civil Engineering, VSB -TU Ostrava. (Fig.1) [14].…”

Section: Introductionmentioning

confidence: 99%

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Vaňuš¹,

Nedoma²,

Fajkus³

et al. 2020

Preprint

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This article introduces a new way of using a Fibre Bragg Grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a Smart Home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an Artificial Neural Network (ANN) with a Scaled Conjugate Gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the Adaptive Filtration with of LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

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“…It contains seven thermal sensation levels: "cold (−3)", "cool (−2)", "slightly cool (−1)", "neutral (0)", "warm (1)", "slightly warm (2)" and "hot(3)", respectively. In [33][34][35], the subjective comfort evaluation methods are given in smart homes. This subjective evaluation level is modified as a subjective comfort level (SCL) to be used for the participant to answer their direct thermal sensation via the online questionnaire with the intention to study the difference in between human's subjective thermal comfort and system's thermal comfort level.…”

Section: Human Thermal Comfortmentioning

confidence: 99%

Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart Homes

Fang

Lim

Ooi

et al. 2020

Sensors

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An environmental thermal comfort model has previously been quantified based on the predicted mean vote (PMV) and the physical sensors parameters, such as temperature, relative humidity, and air speed in the indoor environment. However, first, the relationship between environmental factors and physiology parameters of the model is not well investigated in the smart home domain. Second, the model that is not mainly for an individual human model leads to the failure of the thermal comfort system to fulfill the human’s comfort preference. In this paper, a cyber–physical human centric system (CPHCS) framework is proposed to take advantage of individual human thermal comfort to improve the human’s thermal comfort level while optimizing the energy consumption at the same time. Besides that, the physiology parameter from the heart rate is well-studied, and its correlation with the environmental factors, i.e., PMV, air speed, temperature, and relative humidity are deeply investigated to reveal the human thermal comfort level of the existing energy efficient thermal comfort control (EETCC) system in the smart home environment. Experimental results reveal that there is a tight correlation between the environmental factors and the physiology parameter (i.e., heart rate) in the aspect of system operational and human perception. Furthermore, this paper also concludes that the current EETCC system is unable to provide the precise need for thermal comfort to the human’s preference.

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Evaluation of thermal comfort of the internal environment in smart home using objective and subjective factors

Cited by 8 publications

References 10 publications

The design of an indirect method for the human presence monitoring in the intelligent building

The design of an indirect method for the human presence monitoring in the intelligent building

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart Homes

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