Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use

Hamed Yassaghi,Simi Hoque

doi:10.3390/en14020367

Abstract

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.

Highlights

Using the morphed TMY3 weather file, adapted with IPCC A2 scenario, there is an increase of 1454.9 GJ in cooling energy use by the end of the century compared to the current available TMY3 file for Philadelphia
For the Principal Component Regression (PCR) we fit distributions of Beta, Fisher-Tippett, GEV, Logistic, Normal and BestFit to the principal components associated to the average annual weather variables
The distribution selection of the weather variables for the multiple linear regression model and the principal component regression model are slightly different. This is due to the parameter fitting test that produced distributions that differed between the weather variables and their corresponding principal components

Summary

Introduction

Due to climate change, existing TMY files (using historical data) should not be used to assess building energy performance for the future [5,6]. Climate Models (RCMs) are coarse in resolution They cannot be used directly in building simulation tools, and need to be downscaled to hourly temporal resolutions. This is done using statistical downscaling techniques (e.g., stochastic weather generators) [7]. Downscaling methods are useful to develop weather files that can be directly incorporated in building energy simulations in order to assess building performance under climate change scenarios [9].

Results

Discussion

Conclusion