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Occupant behaviour on air conditioning directly influences building energy consumption. Current building energy simulation typically assumes that occupants operate air conditioners ideally ignoring the stochastic characteristic of occupant behaviour, which leads to a large deviation of predicted energy performance from real consumption. This paper proposed a methodology framework for modelling occupants’ stochastic behaviour on air conditioning. This method combined measured data, statistical analysis and logistic regression to derive stochastic behaviour models that give the probability of when and above which temperature occupants may turn on air conditioning, at what temperature they may set and when they may turn off air conditioning. The behaviour models developed in this paper were further incorporated in widely used building simulation engine—EnergyPlus. The accuracy of the developed simulation model was validated against field measurement. Using this model, the cooling energy performance was simulated and compared with results from building energy standard based settings. The result shows that simulation using settings according to building energy standard over-predicts cooling load and total cooling energy requirements by 113% and 5.6 times respectively, which are very large discrepancies and may lead to a misunderstanding of energy savings potential of energy efficiency measures. The methodology developed in this paper has generality and can be extended and applied in other buildings for constructing behaviour models of air conditioning. The limitations of the current research as well as future research works were also discussed. [ABSTRACT FROM AUTHOR]

The paper focuses on the assessment of the windows’ contribution to the energy performance of buildings in the warmer regions of Europe. This is derived by calculating the cooling energy index q c and the area weighted energy needs for different window configurations installed on a reference room for two different building uses, office and residential. The examined window types differed with regard to their thermophysical and optical properties ( U - and g -value), geometry (frame and window fractions) and orientations. The energy parameters used for the performance assessment were calculated with the help of a dynamic simulation tool for a location with a representative Mediterranean climate. The extensive multi-parametric analysis exhibited interesting results. It was found that for the cooling mode the energy performance of windows in warm climates is influenced significantly by their thermophysical properties. More specifically, the impact of solar transmittance is significant and its optimal selection can contribute in minimizing the energy consumption, especially in environments with controlled ventilation, such as offices. On the contrary, advanced fenestration products with low thermal transmittance seem to behave unfavorably, since their extremely low thermal transmittance prohibits the dissipation of heat toward the ambient environment and results ultimately in higher cooling energy loads. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of the energy efficiency of a conventional ventilating and air conditioning (VAC) system with three configurations. This system was used to cool an experimental room in an office building in more control scenarios realised by adopting different cooled water temperatures and chiller cooling systems. In this purpose, an air–water mist cooled system for an air-cooled chiller is proposed. Depending on the components, the analysed VAC system has the following configurations: (1) air handling unit (AHU) and fan-coil units (FCUs); (2) air handling unit (AHU); and (3) heat recovery unit (HRU) and fan-coil units (FCUs). The electricity consumption weight of the equipment of each VAC system, the total energy consumption and the influence of control parameters on this consumption are analysed. The results of the experiments indicate that the optimal efficiency VAC system in office buildings includes an AHU and FCUs with an air–water mist cooled chiller and a cooled water temperature of 8 °C. For this system, a mathematical model is developed to minimise the electricity consumption by optimal control, where the consumption depends only on non-controlled parameters (solar radiation intensity and outdoor air temperature). This model is verified with actual measurements to confirm its accuracy, and a good agreement between the prediction and measurement is achieved. Additionally, the assessment of thermal comfort in the experimental office is performed based on the PMV-PPD model using the ASHRAE Thermal Comfort programme. The obtained results demonstrate that the AHU&FCUs system assures an increase in thermal comfort. Finally, a TRNSYS simulation model of energy consumption and PMV-PPD indices for the AHU&FCUs system is developed and experimentally validated. [ABSTRACT FROM AUTHOR]

Abstract: This paper described the development of regression models which are able to predict office building annual heating, cooling and auxiliary energy requirements for different HVAC systems as a function of office building heating and cooling demands. In order to represent the office building stock, a large number of building parameters were explored such as built forms, fabrics, glazing levels and orientation. Selected parameters were combined into a large set of office building models (3840 in total). As different HVAC systems have different energy requirements when responding to same building demands, each of the 3840 models were further coupled with five HVAC systems: VAV, CAV, fan-coil system with dedicated air (FC), and two chilled ceiling systems with dedicated air, radiator heating and either embedded pipes (EMB) or exposed aluminium panels (ALU). In total 23,040 possible scenarios were created and simulated using EnergyPlus software. The annual heating and cooling demands and their HVAC system''s heating, cooling and auxiliary energy requirements were normalised per floor area and fitted to two groups of statistical models. Outputs from the regression analysis were evaluated by inspecting models best fit parameter values and goodness of fit. Based on the described analysis, the specific regression models were recommended. [Copyright &y& Elsevier]