Publications
2022
Yassaghi, Hamed; Mostafavi, Nariman; Wen, Jin; Hoque, Simi
Partitioning Climate, Users, and Thermophysical Uncertainties from Building Energy Use: A Monte Carlo & ANOVA Approach Miscellaneous
2022, ISSN: 2075-5309.
Abstract | Links | BibTeX | Tags: climate change; building and user factors; partitioning uncertainties; Monte Carlo; ANOVA
@misc{Yassaghi2022,
title = {Partitioning Climate, Users, and Thermophysical Uncertainties from Building Energy Use: A Monte Carlo & ANOVA Approach},
author = {Hamed Yassaghi and Nariman Mostafavi and Jin Wen and Simi Hoque},
url = {https://doi.org/10.3390/buildings12020095},
doi = {10.3390/buildings12020095},
issn = {2075-5309},
year = {2022},
date = {2022-01-01},
volume = {12},
number = {2},
abstract = {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.},
keywords = {climate change; building and user factors; partitioning uncertainties; Monte Carlo; ANOVA},
pubstate = {published},
tppubtype = {misc}
}
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.