TY - JOUR
T1 - A review of uncertainty analysis in building energy assessment
AU - Tian, Wei
AU - Heo, Yeonsook
AU - de Wilde, Pieter
AU - Li, Zhanyong
AU - Yan, Da
AU - Park, Cheol Soo
AU - Feng, Xiaohang
AU - Augenbroe, Godfried
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China (No. 51778416 ) and the Key Projects of Philosophy and Social Sciences Research, Ministry of Education (China) “Research on Green Design in Sustainable Development” (contract No. 16JZDH014, approval No. 16JZD014). The research was also supported by the Program for Innovative Research Team in University of Tianjin (No. TD13-5012/5045 ).
Funding Information:
This research was supported by the National Natural Science Foundation of China (No. 51778416) and the Key Projects of Philosophy and Social Sciences Research, Ministry of Education (China) “Research on Green Design in Sustainable Development” (contract No. 16JZDH014, approval No. 16JZD014). The research was also supported by the Program for Innovative Research Team in University of Tianjin (No. TD13-5012/5045).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/10
Y1 - 2018/10
N2 - Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.
AB - Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.
KW - Bayesian computation
KW - Building energy
KW - Inverse problems
KW - Uncertainty analysis
KW - Uncertainty propagation
UR - http://www.scopus.com/inward/record.url?scp=85047631496&partnerID=8YFLogxK
U2 - 10.1016/j.rser.2018.05.029
DO - 10.1016/j.rser.2018.05.029
M3 - Review article
AN - SCOPUS:85047631496
SN - 1364-0321
VL - 93
SP - 285
EP - 301
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
ER -