Analysis of the Thermal Comfort and Energy Performance of a Thermal Chair for Open Plan Office

Shahzad, Sally; Calautit, Katrina; Wei, Shuangyu; Tien, Paige Wenbin; Calautit, John Kaiser; Hughes, Ben Richard

doi:10.13044/j.sdewes.d7.0298

Cited by 18 publications

(15 citation statements)

References 39 publications

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“…From a thermal comfort perspective, there is increased interest in indoor environments which do not provide static, isothermal conditions on a room/dwelling basis [12]. Presently, UK domestic temperatures tend to be controlled by gas central heating systems operated by a single thermostat [13]; UK offices are typically regulated by centrally controlled air supply systems (HVAC), which lack opportunities for personal control [14]. The deployment of personal comfort systems allows the user to tune their local environment to their personal preferences, against a background heating or cooling load which would provide minimal comfort by itself.…”

Section: Introductionmentioning

confidence: 99%

“…This may take the form of heated seating, or air-flow control systems, which are provided at the user's workspace. Such systems have the potential to reduce heating demand considerably [15,14]. Heterogeneous indoor microclimates provided by such systems could be deliberately constructed to introduce thermal asymmetries and local air movement [16].…”

Section: Introductionmentioning

confidence: 99%

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The associations between thermal variety and health: Implications for space heating energy use

et al. 2020

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Fossil fuels dominate domestic heating in temperate climates. In the EU, domestic space heating accounts for around 20% of final energy demand. Reducing domestic demand temperatures would reduce energy demand. However, cold exposure has been shown to be associated with adverse health conditions. Using an observational dataset of 77,762 UK Biobank participants, we examine the standard deviation of experienced temperature (named here thermal variety) measured by a wrist worn activity and temperature monitor. After controlling for covariates such as age, activity level and obesity, we show that thermal variety is 0.15˚C 95% CI [0.07-0.23] higher for participants whose health satisfaction was 'extremely happy' compared to 'extremely unhappy'. Higher thermal variety is also associated with a lower risk of having morbidities related to excess winter deaths. We argue that significant CO 2 savings would be made by increasing thermal variety and reducing domestic demand temperatures in the healthiest homes. However, great care is needed to avoid secondary health impacts due to mould and damp. Vulnerable households should receive increased attention.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The associations between thermal variety and health: Implications for space heating energy use

et al. 2020

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“…For the simulation of the building, Integrated Environment Solutions Virtual Environment was employed as the BES tool in this study. Our previous works 27,28 detail the tool's validation and the relevant theory. The geometry was modelled in the ModelIT tool, and some simplifications were carried out, including internal features such as furniture and external features such as surrounding buildings and trees were not included.…”

Section: Performance Evaluation and Building Energy Simulationmentioning

confidence: 99%

The impact of deep learning–based equipment usage detection on building energy demand estimation

Wei

Tien

et al. 2021

Building Services Engineering Research and Technology

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As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

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“…Heat transfer processes of conduction, convection, and radiation between each building fabric were modelled and included in the modelling of air exchange and heat gains within and around the building's selected thermal space. The equations are fully detailed in our previous work [70,71]. The DLIP building occupancy profile was compared with three other profiles; the actual observation profile, and two conventional fixed schedule profiles, Typical Office Profiles 1 and 2.…”

Section: Application Of the Deep Learning Modelmentioning

confidence: 99%

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

Tien

Wei²,

Calautit³

et al. 2020

J. sustain. dev. energy water environ. syst.

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The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both 'static' and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems.

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Analysis of the Thermal Comfort and Energy Performance of a Thermal Chair for Open Plan Office

Cited by 18 publications

References 39 publications

The associations between thermal variety and health: Implications for space heating energy use

The associations between thermal variety and health: Implications for space heating energy use

The impact of deep learning–based equipment usage detection on building energy demand estimation

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

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