A review of uncertainty analysis in building energy assessment

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:93

Review

Tian, Wei^1,2  Heo, Yeonsook³  de Wilde, Pieter⁴  Li, Zhanyong^1,2  Yan, Da⁵  Park, Cheol Soo⁶  Feng, Xiaohang⁵  Augenbroe, Godfried⁷ 

[1] Tianjin Univ Sci & Technol, Coll Mech Engn, Tianjin Key Lab Integrated Design & Online Monito, Tianjin 300222, Peoples R China

[2] Tianjin Int Joint Res & Dev Ctr Low Carbon Green, Tianjin 300222, Peoples R China

[3] Korea Univ, Coll Engn, Sch Civil Environm & Architectural Engn, Seoul, South Korea

[4] Univ Plymouth, Chair Bldg Performance Anal, Environm Bldg Grp, Plymouth PL4 8AA, Devon, England

[5] Tsinghua Univ, Sch Architecture, Beijing 100084, Peoples R China

[6] Seoul Natl Univ, Coll Engn, Dept Architecture & Architectural Engn, Seoul 08826, South Korea

[7] Georgia Inst Technol, Coll Architecture, Atlanta, GA 30332 USA

关键词: Building energy;    Uncertainty analysis;    Uncertainty propagation;    Inverse problems;    Bayesian computation;   

DOI  :  10.1016/j.rser.2018.05.029

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free