Measured Indoor Temperatures, Thermal Comfort and Overheating Risk: Post-occupancy Evaluation of Low Energy Houses in the UK

Jones, Raymond C. F.; Goodhew, Steve; Wilde, Pieter de

doi:10.1016/j.egypro.2016.06.049

Cited by 43 publications

(33 citation statements)

References 10 publications

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“…Discomfort from humidity is only expected as a result of sweating prompted by high temperatures or humidities outside of the 40-70% range. This could offer an explanation for the current findings, as some of the dwellings investigated in this study have previously been shown to exceed recommended indoor temperatures and be at risk of overheating in summer [43]. In addition, the descriptive statistics reported in this paper show that in some instances, the humidities were also outside the acceptable range: 32-88% (indoor) and 36-99% (outdoor).…”

Section: Discussionmentioning

confidence: 49%

“…The data hubs exported the data to a remote server every hour using General Packet Radio Service (GPRS), which was accessed by the researchers on the Internet. The data used in this study were collected as part of a larger Post-Occupancy Evaluation (POE) to assess the actual operational performance of the dwellings [43,44]. The variables used in this paper were measured continuously for all dwellings from 28 th October 2013 to 2 nd November 2014 (370 days).…”

Section: Measurementsmentioning

confidence: 99%

“…The data used in this study were collected as part of a larger Post-Occupancy Evaluation (POE) to assess the actual operational performance of the dwellings [43,44], rather than a specific study of occupant's window behaviour, therefore the range of indoor and outdoor environment factors used for modelling (i.e. indoor air temperature and relative humidity; outdoor air temperature and relative humidity; wind speed; global solar radiation; and rainfall) were those available to the researchers.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Jones

Fuertes

Gregori

et al. 2017

Building and Environment

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100

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This paper presents the development of stochastic models of occupants' main bedroom window operation based on measurements collected in ten UK dwellings over a period of a year. The study uses multivariate logistic regression to understand the probability of opening and closing windows based on indoor and outdoor environment factors (physical environmental drivers) and according to the time of the day and season (contextual drivers). To the authors' knowledge, these are the first models of window opening and closing behaviour developed for UK residential buildings. The work reported in this paper suggests that occupants' main bedroom window operation is influenced by a range of physical environmental (i.e. indoor and outdoor air temperature and relative humidity, wind speed, solar radiation and rainfall) and contextual variables (i.e. time of day and season). In addition, the effects of the physical environmental variables were observed to vary in relation to the contextual factors. The models provided in this work can be used to calculate the probability that the main bedroom window will be opened or closed in the next 10 minutes. These models could be used in building performance simulation applications to improve the inputs for occupants' window opening and closing behaviour and thus the predictions of energy use and indoor environmental conditions of residential buildings.

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Section: Discussionmentioning

confidence: 49%

Section: Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Jones

Fuertes

Gregori

et al. 2017

Building and Environment

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100

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“…Some research on this show that it produces a comfort zone even in a different space [11]. Similar research discovers that it also generates different occupants' thermal comfort in each space [12].…”

Section: Introductionmentioning

confidence: 99%

The comparison of vernacular residences’ thermal comfort in coastal with that in mountainous regions of tropical areas

Hermawan

Prianto

Setyowati

et al. 2017

AIP Conference Proceedings

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“…Internet. The data used in this study were collected as part of a larger Post Occupancy Evaluation (POE) to assess 141 the actual operational performance of the dwellings [33,41,42].…”

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confidence: 99%

Physical environmental and contextual drivers of occupants’ manual space heating override behaviour in UK residential buildings

Bruce-Konuah

Jones

Fuertes

2019

Energy and Buildings

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Measured Indoor Temperatures, Thermal Comfort and Overheating Risk: Post-occupancy Evaluation of Low Energy Houses in the UK

Cited by 43 publications

References 10 publications

Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

The comparison of vernacular residences’ thermal comfort in coastal with that in mountainous regions of tropical areas

Physical environmental and contextual drivers of occupants’ manual space heating override behaviour in UK residential buildings

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