The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature

Han, Jinmog; Bae, Jongkyun; Jang, Jinyoung; Baek, Jumi; Leigh, Seung Bok

doi:10.3390/su11195417

Cited by 15 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the MRT of the space, the temperature emitted from the surface can be measured by installing a globe temperature sensor at the center of the space. It is easier to obtain the MRT by using a globe temperature sensor than by calculation; however, owing to the nature of the globe temperature sensor, there are practical difficulties in installing and using it in an indoor space [40,41]. To overcome this limitation, this study aimed to obtain a value that can replace the MRT by using an IR array sensor.…”

Section: Resultsmentioning

confidence: 99%

Collection and Utilization of Indoor Environmental Quality Information Using Affordable Image Sensing Technology

et al. 2022

Self Cite

View full text Add to dashboard Cite

Incorporating Internet of Things (IoT) technology into the operation of buildings is expected to generate immense synergy, thereby saving energy and improving occupant comfort by overcoming the limitations of the existing system. Preventing operations in the absence of occupants can save energy, and the occupants’ preferred operating temperature should be used as the control set-point rather than the nominal temperature. In this study, IoT technology and image sensors are used to rapidly detect indoor environment changes, and a method is proposed to utilize air quality and thermal comfort as the control set-points. A real-time ventilation control algorithm is proposed based on the CO2 concentration calculated according to the number of occupants. To check the thermal comfort level, the real-time operating temperature estimated from the surface temperature data of the infrared array sensor is reflected in the comfort zone defined by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The deficiencies in indoor environment conditions caused by the temporal and spatial lag of sensors in the old system are minimized using IoT technology, which also facilitates wireless communications. The image sensors can be used for multiple purposes based on various interpretations of the image information obtained.

show abstract

Section: Resultsmentioning

confidence: 99%

Collection and Utilization of Indoor Environmental Quality Information Using Affordable Image Sensing Technology

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…An EMS requires various outdoor and indoor environmental data, and should be considered with care in building energy analysis because it is closely related to building energy and resident comfort [27]. Indoor environmental data-that is, indoor dry-bulb temperature, relative humidity, air quality, etc.-were generally collected in real time using various sensors installed inside the building [28].…”

Section: Time Datamentioning

confidence: 99%

Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

et al. 2021

Self Cite

View full text Add to dashboard Cite

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.

show abstract

“…These efforts focus on domains like cost and time efficiency [9], safety of systems [10], material improvement [11,12], etc. Likewise, energy analysis has been of great interest and experts have tried to employ the latest technologies in this domain [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

et al. 2022

View full text Add to dashboard Cite

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.

show abstract

The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature

Cited by 15 publications

References 24 publications

Collection and Utilization of Indoor Environmental Quality Information Using Affordable Image Sensing Technology

Collection and Utilization of Indoor Environmental Quality Information Using Affordable Image Sensing Technology

Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Contact Info

Product

Resources

About