Berkeley thermal comfort models: Comparison to people votes and indications for user-centric HVAC strategies in car cabins

Jaoude, Rachelle Abou; Thiagalingam, Ilango; Khoury, Roch El; Crehan, Gabriel

doi:10.1016/j.buildenv.2020.107093

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…The weights also reflect local asymmetries in thermal sensation (32). The global sensation is measured on a 9-point ASHRAE scale ranging from −4 (very cold) to 4 (very hot) (33).…”

Section: Berkeley Thermal Comfort Evaluation Modelmentioning

confidence: 99%

The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure

Yang¹,

Zhou²,

Xu³

et al. 2023

Front. Public Health

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IntroductionProlonged exposure of train drivers to thermal discomfort can lead to occupational safety and health (OSH) risks, causing physical and mental injuries. Traditional method of treating human skin as a wall surface fail to observe accurate skin temperature changes or obtain human thermal comfort that adapts to the thermal environment.MethodsThis study employs the Stolwijk human thermal regulation model to investigate and optimize the thermal comfort of train drivers. To minimize the time-consuming design optimization, a pointer optimization algorithm based on radial basis function (RBF) approximation was utilized to optimize the train cab ventilation system design and enhance drivers’ thermal comfort. The train driver thermal comfort model was developed using Star-CCM+ and 60 operating conditions were sampled using an Optimal Latin Hypercube Design (Opt LHD).Results and DiscussionWe analyzed the effects of air supply temperature, air supply volume, air supply angle, solar radiation intensity and solar altitude angle on the local thermal sensation vote (LTSV) and overall thermal sensation vote (OTSV) of the train driver. Finally, the study obtained the optimal air supply parameters for the Heating Ventilation and Air Conditioning (HVAC) in the train cabin under extreme summer conditions, effectively improving the thermal comfort of the driver.

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“…The weights also reflect local asymmetries in thermal sensation (32). The global sensation is measured on a 9-point ASHRAE scale ranging from −4 (very cold) to 4 (very hot) (33).…”

Section: Berkeley Thermal Comfort Evaluation Modelmentioning

confidence: 99%

The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure

Yang¹,

Zhou²,

Xu³

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Additional variations can ensue with time due to changes in outer temperature, solar radiation through the windows and external air flow through the doors [3,5,13]. To better capture these phenomena, dynamic thermophysiological models, such as the twonodes model [3,8,9,14] or local models [15,16], are sometimes used. However, the accuracy of such models for bus cabins is unknown due to the limited validation by comparison with empirical results from, e.g., passengers' surveys.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Santoyo Alum,

Fay,

Cigarini

et al. 2023

Applied Sciences

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In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers’ thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift.

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“…[7][8][9][10] Individual as well as public transportation ranging from cars to aircraft cabins also spiked research interest. [11][12][13] Local climatization reduces energy consumption and at the same time allows for a better accommodation to individual thermal preferences. Typically, large air volumes are conditioned and provided to building zones or rooms.…”

Section: Introductionmentioning

confidence: 99%

“…7–10 Individual as well as public transportation ranging from cars to aircraft cabins also spiked research interest. 11–13…”

Section: Introductionmentioning

confidence: 99%

Modular personalized climatization testing infrastructure with smartphone-based user feedback

Metzmacher

Syndicus

Warthmann

et al. 2022

Building Services Engineering Research and Technology

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Heating and ventilation in buildings and vehicles are responsible for over a third of global final energy usage and the resulting emissions. Local climatization can help to save energy while at the same time enable more individualized and adapted micro-climates around people. For the domain of local comfort, integrative test environments for sensors, actuators, and control software are scarce, and oftentimes tailored to a specific set of components. Here, a server-based modular testing infrastructure which allows integration and evaluation of sensors, actuators, and control strategies is presented. Currently, the system is able to integrate, monitor, and log data of thermal imaging, motion sensing, environmental sensor such as temperature or air velocity and to forward signals to actuators such as fans, infrared- or contact-heaters. The generation of control signals is model-based and relies on user feedback provided via the system’s smartphone app. Lastly, learning algorithms can be trained and compared during user studies. Practical Application: Although developed in a laboratory based research context, the proposed system is based on open standards and protocols. It therefore can be applied by practitioners, developers, and manufacturers in order to test stand-alone components as well as ensembles of sensors and actuators for personalized climatization in an integrative and replicable manner.

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Berkeley thermal comfort models: Comparison to people votes and indications for user-centric HVAC strategies in car cabins

Cited by 13 publications

References 22 publications

The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure

The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

Modular personalized climatization testing infrastructure with smartphone-based user feedback

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