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Shopping complexes are widely built for their convenience and multiple functions. However, their complex functional areas, result in significantly different thermal environments and various customers’ thermal perception. To explain the influence of functional related parameters on the thermal perception of customers in shopping complexes, we selected two typical functional related parameters, including customers’ thermal expectation level of indoor environment in their current area and the area where the customer was ten minutes before taking the survey. 851 valid questionnaires were obtained in two typical shopping complexes during July. Customers’ thermal neutral temperature, thermal preference temperature and thermal comfort temperature range were calculated in different functional areas. Customers’ thermal expectation level was quantified by using expectancy factor. The results showed that customers’ thermal expectation level in entertainment areas was the highest, followed by food courts, retail areas, and transition spaces. Customers’ thermal expectation level would influence their thermal neutral temperature and thermal sensitivity. Customers with different thermal experience differed significantly in their thermal sensation voting (p< 0.01). The highest thermal sensitivity, about 0.41/°C, was found in customers moving from high-temperature areas to low-temperature areas. These findings help to clarify how functional related parameters affect the thermal comfort of customers and provide the guidance for designing the indoor temperature in shopping complexes.

Abstract: The assessment of moderate thermal environments provided with HVAC system is usually carried out by means of the PMV and the PPD indices. The reliability of the PMV to predict the thermal sensation in non-air conditioned environments has been criticized in the past. Particularly, Fanger himself, realizing the difference in the expectations between people not used to occupy conditioned environments and people used to, proposed to widen the use of PMV even in non-air conditioned environments by means of an expectancy factor. This factor, multiplied by the PMV value, returns a corrected vote that keeps into account the difference in expectancy of occupants of non-air conditioned environments. In this paper an expectancy factor value for the Mediterranean climate is proposed. This value has been obtained by carrying out a combined subjective and objective investigation on about 200 Italian classrooms and more than 4000 students both in winter and in summer season. All investigated school buildings were non-air conditioned and ventilated by operable windows. The subjective investigation has been carried out by means of a special questionnaire also containing a direct question on the acceptability to verify the agreement between the percentages of dissatisfied consistent with the votes assigned to the thermal environment and the acceptability. Obtained results seem to show similar distributions with only a slight overestimation (15% about) of the percentage of dissatisfied under neutrality conditions. [Copyright &y& Elsevier]

Deficient performance of Predicted Mean Vote (PMV) in estimating thermal sensation in warm environments has always been considered as one of the major drawbacks of this model. This well-known shortcoming is often attributed to PMV overlooking thermal adaptation, but simplistic treatment of thermoregulatory sweating may also partly contribute to this error. In this paper a new approach to estimating sweating heat loss in the PMV model is proposed based on a piecewise fuzzy regression model. The effect of sweating at low physical activity levels and the impact of environmental parameters on whole-body evaporative heat loss are also accounted for. The new modified PMV model is validated with previously published experimental data. The result of our analysis demonstrated that the proposed model performs better than the original PMV model. The modified PMV model predicted thermal sensations more accurately than the original version of the PMV model, even without the expectancy factor being applied. This implies that a considerable part of the PMV error is associated with the simplification of sweating calculations. Ignoring the structural drawback of the original PMV calculations can lead to over-estimation of adaptation (or expectancy) coefficients when adjusting the PMV model. • Improvement of the Fanger's model by modification of regulatory sweating heat loss. • Considering the impacts of environmental parameters on sweating. • Estimating the contribution of regulatory sweating at low body activities. • Performance improvement of Fanger's model for using in warm climatic conditions. • Necessity of structural revisions of PMV model before finding of expectancy factors. [ABSTRACT FROM AUTHOR]

Building energy assessment software/programs use various assumptions and types of thermal comfort models to forecast energy consumption. This paper compares the results of using two major thermal comfort models (adaptive thermal comfort and the predicted mean vote (PMV) adjusted by the expectancy factor) to examine their influence on the prediction of the energy consumption for several full-scale housing experimental modules constructed on the campus of the University of Newcastle, Australia. Four test modules integrating a variety of walling types (insulated cavity brick (InsCB), cavity brick (CB), insulated reverse brick veneer (InsRBV), and insulated brick veneer (InsBV)) were used for comparing the time necessary for cooling and heating to maintain internal thermal comfort for both models. This research paper exhibits the benefits of adopting the adaptive thermal model for building structures. It shows the effectiveness of this model in helping to reduce energy consumption, increasing the thermal comfort level for the buildings, and therefore reducing greenhouse emissions. [ABSTRACT FROM AUTHOR]