Comparison of thermal comfort algorithms in naturally ventilated office buildings

Moujalled, Bassam; Cantin, Richard; Guarracino, Gérard

doi:10.1016/j.enbuild.2008.06.014

Cited by 79 publications

(36 citation statements)

References 6 publications

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“…The standard was based on the database compiled from several countries worldwide and has proven to be more reliable than the Predicted Mean Vote (PMV) index in free running buildings [37][38][39]. Moreover, its applicability in the cold climate zone of China has also been recently verified [40].…”

Section: Thermal Comfort Calculationmentioning

confidence: 99%

The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China

et al. 2017

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This paper discusses the role of geometry parameters including building shape, window to wall ratio, room depth, and orientation on the energy use and thermal comfort of school buildings in cold climates of China. The annual total energy demand and summer thermal discomfort time were compared through computer simulations with DesignBuilder. Furthermore, a questionnaire was conducted that related to the students' subjective preference for various building geometry parameters. Results showed that a maximum of 13.6% of energy savings and 3.8% of thermal comfort improvement when compared to the reference case could be achieved through variations in geometry parameters. The H shape performed the best when the building thermal performance and students' preferences were considered, as well as the various design options for architects. Window to wall ratio, room depth, and orientation should also be carefully addressed in terms of different building types. The results of this study can serve as a reference for architects and school managers in the early design stages of schools in cold climates of China.

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Section: Thermal Comfort Calculationmentioning

confidence: 99%

The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China

et al. 2017

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“…In naturally ventilated buildings, indoor temperatures fluctuate in response to the natural swings of the outdoor and indoor climate [28]. During fall the indoor air temperature ranged between 24.5 and 30.…”

Section: Seasonal Variation In Indoor Temperature and Thermal Sensationmentioning

confidence: 99%

Influence of seasonal variation on thermal comfort and ventilation rates in Gaza Strip climate

Elbayoumi¹,

Ramli²,

Yusof³

et al. 2014

Turkish J Eng Env Sci

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Abstract:The indoor air quality (IAQ) and thermal comfort in classrooms highly affect the health and productivity of children. Concerns have been raised regarding whether seasonal variation may affect thermal comfort and ventilation rate. In a two-season study of ventilation and thermal comfort of 36 classrooms in 12 naturally ventilated schools in Gaza, Palestine, ventilation rates and thermal comfort were measured. Data on environmental perception were obtained from 724 students by using a validated questionnaire. The results showed significant seasonal variation in perceived indoor environment and thermal comfort in the monitored schools. Differences in neutral temperature between seasons were also observed. Moreover, 83.3% of the classrooms presented a mean ventilation rate lower than 7.5 L/s per person in winter. During fall, only 50% of the measured classrooms presented a flow rate higher than the recommended value.Furthermore, there was a considerable increase in the carbon dioxide level in winter relative to fall. As vulnerable students, this situation negatively affects their performance and health. Therefore, mechanical ventilation systems are needed to provide a dependable and continuous supply of outdoor air.

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“…1 much of the previous work in this area has tended to view the user as the average human being and so the entire active feedback loop is replaced with an open loop lookup table. These tables are derived typically from the ASHRAE standard [8,9] which defines thermal comfort using the Predicted Mean Vote (PMV) [10] and associated Predicted Percent of users Dissatisfied (PPD). In order to fully estimate these quantities one must also measure the clothing index, metabolic rate, gender, and other factors which are not possible in a ubiquitous environment [11] (see [12] for a indepth study which does identify factors which would be appropriate for ubiquitous systems to measure).…”

Section: Related Workmentioning

confidence: 99%

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Fay

O'Toole

Brown

2017

Pervasive and Mobile Computing

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a b s t r a c tThis paper presents a ubiquitous thermal comfort preference learning study in a noisy environment. We introduce Gaussian Process models into this field and show they are ideal, allowing rejection of outliers, deadband samples, and produce excellent estimates of a users preference function. In addition, informative combinations of users preferences becomes possible, some of which demonstrate well defined maxima ideal for control signals. Interestingly, while those users studied have differing preferences, their hyperparameters are concentrated allowing priors for new users. In addition, we present an active learning algorithm which estimates when to poll users to maximise the information returned.

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Comparison of thermal comfort algorithms in naturally ventilated office buildings

Cited by 79 publications

References 6 publications

The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China

The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China

Influence of seasonal variation on thermal comfort and ventilation rates in Gaza Strip climate

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

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