283 research outputs found

    Thermal performance of a naturally ventilated building using a combined algorithm of probabilistic occupant behaviour and deterministic heat and mass balance models

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    This study explores the role of occupant behaviour in relation to natural ventilation and its effects on summer thermal performance of naturally ventillated buildings. We develop a behavioural algorithm (the Yun algorithm) representing probablistic occupant behaviour and implement this within a dynamic energy simulation tool. A core of this algorithm is the use of Markov chain and Monte Carlo methods in order to integrate probablistic window use models into dynamic energy simulation procedures. The comparison between predicted and monitored window use patterns shows good agreement. Performance of the Yn algorithm is demonstrated for active, medium and passive window users and a range of office constructions. Results indicate, for example, that in some cases, the temperature of an office occupied by the active window user in summer is up to 2.6ºC lower than that for the passive window user. A comparison is made with results from an alernative bahavioural algorithm developed by Humphreys [H.B. Rijal, P. Tuohy, M.A. Humphreys, J.F. Nicol, A. Samual, J. Clarke, Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings, Energy and Buildings 39(7)(2007) 823-836.]. In general, the two algorithms lead to similar predictions, but the results suggest that the Yun algorithm better reflects the observed time of day effects on window use (i.e. the increased probability of action on arrival)

    Effects of microclimate and human parameters on outdoor thermal sensation in the high-density tropical context of Dhaka

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    open access articleA thermal comfort questionnaire survey was carried out in the high-density, tropical city Dhaka. Comfort responses from over 1300 subjects were collected at six different sites, alongside meteorological parameters. The effect of personal and psychological parameters was examined in order to develop predictive models. Personal parameters included gender, age, activity, professiontype (indoor or outdoor-based), exposure to air-conditioned space and sweat-levels. Psychological parameters, such as ‘the reason for visiting the place’ and ‘next destination is air-conditioned’, had statistically significant effects on thermal sensation. Other parameters, such as ‘body type’, ‘body exposure to sun’, ‘time living in Dhaka’, ‘travelling in last_30 min’, and ‘hot food’ did not have any significant impact. Respondents’ humidity, wind speed and solar radiation sensation had profound impacts and people were found willing to adjust to the thermal situations with adaptive behaviour. Based on actual sensation votes from the survey, empirical models are developed to predict outdoor thermal sensation in the case study areas. Ordinal linear regression techniques are applied for predicting thermal sensation by considering meteorological and personal conditions of the field survey. The inclusion of personal and weather opinion factors produced an improvement in models based on meteorological factors. The models were compared with the actual thermal sensation using the cross-tabulation technique. The predictivity of the three models (meteorological, thermos-physiological and combined parameter) as expressed by the gamma coefficient were 0.575, 0.636 and 0.727, respectively. In all three models, better predictability was observed in the ‘Slightly Warm’ (71% in meteorological model) and ‘Hot’ (64.9% in combined parameter model) categories—the most important ones in a hot-humid climate
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