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At present, dynamic simulation and analysis on building thermal environment and energy consumption achieved by computer software become a reality. However, it is found that defined gaps exist between simulation and on-site measurement. To explore the gaps, this paper takes typical air-conditioned university dormitories located in Chongqing as a case study. Simultaneously, the gaps are explored with a DeST-h building energy simulation model and on-site measurement. Indicators including indoor temperature, relative humidity and air conditioning energy consumption under different setting temperature are simulated and measured. Then, a comparison between simulation results and measured data is explored as well as the cause of these gaps. This paper studies thermal environment and energy consumption by combining on-site measurement and numerical simulation, and the conclusion is that the gaps can from occupant behaviour and physical parameters of the indoor and outdoor environment, which provides a reference for thermal environment of the dormitory and building simulation. [ABSTRACT FROM AUTHOR]

In this paper simulation results of two typical houses in St. John's are presented using BEOpt (Building Energy optimization) software. Simulation is done using one-year weather data, typical thermostat settings and occupation of two houses. BEopt provides capabilities to evaluate residential building designs and identify cost-optimal solution at various levels of whole-house energy savings along the path to zero net energy. It was noticed that the annual energy consumption determined using simulation in BEopt is almost same as the actual energy consumption, logged with a sampling time of 2 minutes for both the houses. This simulation, analysis and a comparison is necessary to validate the thermal model of houses. The developed model of the houses could be used to design a renewable energy system or study human behaviour impact on the house energy consumption. Simulation results and a detailed analysis of the logged data are presented in the paper. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy assessment to provide reliable analysis of importance ranking for input variables in buildings. Most of previous studies used one single batch simulation models to obtain sensitivity indicators. To conduct robust analysis, this paper implements a sequential sensitivity analysis to assess the convergence of sensitivity indicators for two global sampling-based sensitivity analysis methods (standardized regression coefficients and meta-modelling Sobol) in an office building. The results indicate that this sequential method can be more effective and robust. In contrast, a single batch sensitivity method for building energy assessment may lead to non-convergence results due to smaller number of simulation models or over-convergence results due to larger number of simulation models. It is also found that the convergence rate for standardized regression methods are slower than that for the meta-modelling Sobol method. [ABSTRACT FROM AUTHOR]

Uncertainty analysis has become an active research area in the field of building energy analysis because most of parameters are inherently uncertain in buildings. Most of previous studies implement probabilistic-based uncertainty analysis, which requires detailed information to define probability for input factors. However, it is hard to obtain detailed information for these inputs, especially at the initial design stage of a building project. Therefore, this paper applies the non-probabilistic Dempster-Shafer theory of evidence approach for uncertainty analysis in an office building using the EnergyPlus program. The results indicate that the Dempster-Shafer theory can be very useful to provide pertinent and reliable results by taking into account the level of available information in the design stage of building projects for sustainable buildings. The reliable machine learning models can be constructed in order to reduce high computational cost of this non-probabilistic uncertainty analysis method. [ABSTRACT FROM AUTHOR]