Introduction

In the context of building performance, the discrepancy between building energy simulation and measured observations is the focus of many studies \cite{Menezes_2012,de_Wilde_2014,Attia_2013,Fedoruk_2015}. Since building energy simulation models  deal with an extensive number of input and parameters, imprecision and lack of  knowledge is inevitable. These short–comes, also known as uncertainties, are  major contributors to the gap between simulated and measured building energy  performance.
However, it is commonly believed that obtaining a correct understanding of imprecisions as well as increasing the knowledge in unknown contributors can result in better predictions. In order to achieve a better understanding of the uncertainties and monitor their effects, various techniques are invented. These methods span from the initial part of collecting data for simulation input to post processing each and every effect of the input parameters. Uncertainty Analysis (UA) deals with quantitative modeling, which cannot be associated with certainty \cite{Kurowicka_2006}.The approach has various applications in risk assessment\cite{Jiang_2013}, reliability assessment\cite{Zhang_2013} and decision-making (decision  support)\cite{Kim_2014}.
The uncertainty in Building Energy Simulation (BES) can be addressed considering various sources. Hopfe and Hensen \cite{Hopfe_2011} assumed three sources: uncertainty in physical, design and scenario parameters. They focused on the physical properties of wall, roof and floor area as physical uncertainties. The uncertainty in design parameters was referred to different design strategies, which are adopted in the planning process. Finally, the scenario uncertainty that is derived the future life of a building. The occupancy and future weather condition are the main causes of these kinds of uncertainties. Fig. 1 displays the different sources of uncertainty and their subcategories in BES.