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Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (building, user, and climate) in the total uncertainty of building energy simulation output results. Our results indicate that the contribution of climate uncertainty increases from current conditions to future climate scenarios for chillers, boilers, fans, and pumps' electricity use. User parameters are the dominant uncertainty factor for total site energy use and fans' electricity use. Moreover, boiler and HVAC energy use are highly sensitive to the shape and range of user and building input factor distributions. We underline the importance of selecting the appropriate distribution for input factors when partitioning the uncertainties of building performance modeling. [ABSTRACT FROM AUTHOR]

Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections. [ABSTRACT FROM AUTHOR]

• An uncertainty propagation method was developed which captures climate uncertainties. • The method is applicable to cases where few future weather files can be developed. • The method can reduce computation requirements following a probabilistic approach. • The impact of equipment modernization using updated design day files is investigated. In the United States, buildings consume a significant portion of total energy use and contribute to carbon emissions. Therefore, evaluating their performance for sustainable design is critical. While building standards are updated frequently to improve building efficiency, the climate is changing and classical (deterministic) approaches to quantifying building energy use may not be accurate. Significant uncertainties exist in current models of future building performance. Probabilistic approaches to evaluate whole building performance and account for climate uncertainties require large input data of climate projections with detailed spatial and temporal resolution and considerable computational resources. This paper offers a four-step process to propagate climate uncertainties from future climate projections into building energy performance. The climate uncertainty propagation method consists of a combination of regression, distribution fitting and random sampling. It has the potential to capture climate uncertainties in building simulation tools and is applicable to cases where limited future weather files can be produced. In addition, we include the use of updated design day files in future building performance analysis. Design days drive equipment modernization impacting whole building energy use under climate change. The Department of Energy office reference buildings for Philadelphia climate conditions are used as the case study. The aim of this research is to generate a probabilistic model that captures future building energy performance trends where can be applicable to regions that have limited future hourly weather files to be incorporated into building simulation tools. The results of this paper are intended to promote a probabilistic approach to account for climate uncertainties and to provide guidance in analyzing buildings projected energy consumption under climate change. [ABSTRACT FROM AUTHOR]

It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated. [ABSTRACT FROM AUTHOR]