The Role of Construction Detailing and Workmanship in Achieving Energy-efficient Buildings

Wang, Qinpeng; Augenbroe, Godfried; Sun, Yuming

doi:10.1061/9780784413517.226

Cited by 11 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in previous studies, the infiltration rate has been ranked as one of the key variables influencing building energy use based on sensitivity analyses [9,70]. Most papers have quantified uncertainty in the infiltration rate on the basis of a collection of measurement data available from existing buildings [9,66,71,72]. Emmerich et al [73] summarised an extensive dataset of existing fan pressurized tests, most of which were sourced from the work by Persily [74].…”

Section: Other Parametersmentioning

confidence: 99%

See 1 more Smart Citation

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

Self Cite

324

122

View full text Add to dashboard Cite

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.

show abstract

Section: Other Parametersmentioning

confidence: 99%

“…He also quantified the effect of poor workmanship on the temperature factor by comparing simulation results with measurements. This approach was adopted in the Moon's paper [71] to investigate the effect of varying quality of thermal bridge workmanship and infiltration rates on the building energy demand.…”

Section: Other Parametersmentioning

confidence: 99%

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

Self Cite

324

122

View full text Add to dashboard Cite

show abstract

“…The most frequent defects related to thermal performance mentioned in the literature are poor installation of insulation elements, gaps in the building's fabric and thermal bridging through structural elements, manifesting themselves in the buildings' envelope. These were identified by studies specifically focused on the impact of defects on the building energy performance [2,40,75]. Interestingly, other studies included in the review, not specifically focused on energy related issues (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…In Europe, the studies focused on commercial, educational, governmental and industrial buildings in Sweden [36,37]; and commercial, educational, governmental, health, industrial and infrastructure projects in the UK [8,9,15,29,39,53,55,69]. At an international level, there are studies investigating quality in commercial, educational, governmental and industrial facilities in Australia [59][60][61]70]; commercial and infrastructure projects in Canada [71,72]; governmental buildings in China [62]; infrastructure projects in Iran [73]; educational buildings in Nigeria [74]; commercial, health, industrial, infrastructure and governmental buildings in Singapore [64][65][66][67]; and commercial, governmental and industrial facilities in the US [26,68,75].…”

Section: Previous Studies Investigating Quality Defects In Constructionmentioning

confidence: 99%

The relationship between quality defects and the thermal performance of buildings

Alencastro

Fuertes

Wilde

2018

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

The construction sector accounts for a significant portion of the total final energy use and carbon emissions worldwide. Despite efforts to reduce energy consumption through energy efficiency improvements in buildings, the measures proposed by the construction sector are falling short. Among several causes which lead buildings to perform differently to what was defined in the design stage, commonly referred to as the 'energy performance gap', the occurrence of quality defects has been acknowledged. This paper aims to identify through an in-depth literature review, quality defects which undermine the thermal performance of buildings by comparing the studies' findings with regard to defect characteristics and attributes; major causes and influencing factors; and their impact on the energy performance of construction projects. This review also aims to highlight areas where more research is needed if the expected thermal performance of buildings is to be achieved. Understanding the generation process and effects of defects on the energy efficiency of buildings can support the implementation of appropriate quality management systems in construction projects and thus contribute to the achievement of the intended energy performance targets.

show abstract

“…By doing so, we investigate the effect of model form uncertainty not at one fixed combination of all other input parameters but explore its effect on all possible combinations of parameter values with the uncertainty range established for them in a UQ repository (Lee et al 2013;Sun et al 2013) and other sources used in this study. For example, we quantify the uncertainties in microclimate variables , convective heat transfer coefficients (Palyvos 2008), material properties (Domínguez-Muñoz et al 2010;Macdonald 2002), infiltration rate (Wang, Augenbroe, and Sun 2014), etc. Parameter uncertainties are explored using 500 samples with Latin Hypercube Sampling (LHS) technique (Wyss and Jorgensen 1998) and are propagated in the Georgia Tech Uncertainty and Risk Analysis Workbench (GURA-W) to obtain building energy predictions.…”

Section: Case Study Descriptionmentioning

confidence: 99%

Quantification of model form uncertainty in the calculation of solar diffuse irradiation on inclined surfaces for building energy simulation

Sun

et al. 2014

Journal of Building Performance Simulation

Self Cite

View full text Add to dashboard Cite

Traditional uncertainty quantification (UQ) in the prediction of building energy consumption has been limited to the propagation of uncertainties in model input parameters. Models by definition ignore, at least to some degree, and, in almost all cases, simplify the physical processes that govern the reality of interest, thereby introducing additional uncertainty in model predictions that cannot be captured as input parameter uncertainty. Quantification of this type of uncertainty (which we will refer to as model form uncertainty) is a necessary step towards the complete UQ of model predictions. This paper introduces a general framework for model form UQ and shows its application to the widely used sky irradiation model developed by Perez et al. [1990. "Modeling Daylight Availability and Irradiance Components from Direct and Global Irradiance." Solar Energy 44 (5): 271-289], which computes solar diffuse irradiation on inclined surfaces. We collected a data set of one-year measurements of solar irradiation at one location in the USA. The measurements were done at surfaces with different tilt angles and orientations, for a wide spectrum of sky conditions. A statistical analysis using both this data set and published studies worldwide suggests that the Perez model performs non-uniformly across different locations and produces a certain bias in its predictions. Based on the same data, we then use a two-phase regression model to express model form uncertainty in the use of the Perez model at this particular location. Using a holdout validation test, we demonstrate that the two-phase regression model considerably reduces the model bias errors and root mean square errors for every tilted surface. Lastly, we discuss the significance of including model form uncertainty in the energy consumption predictions obtained with whole building simulation. IntroductionOver the last four decades, scientists and engineers have made significant progress in building energy simulation. Complex buildings now can be described with computational models that simulate realistic performance, such as energy consumption. The role of simulation has been firmly established in the architecture, engineering and construction industry. It is used to inform decisions at scales as large as national energy policy measures and as small as the selection of shading devices of a residential house. Questions about the accuracy of computational models to support such decisions have given rise to attempts to quantify the uncertainty in their outcomes. Based on an appropriately quantified uncertainty, one will be able to decide what level of confidence in the simulation results is warranted.The concept of uncertainty is not unfamiliar to building performance modelling and simulation. It has been introduced since the early 1980s when most efforts were dedicated to model development and validation. One of the best known projects was the Building Energy Simulation Test (BESTEST) (Judkoff, Wortman, and Burch 1983) in which uncertainties of model input parameters

show abstract

The Role of Construction Detailing and Workmanship in Achieving Energy-efficient Buildings

Cited by 11 publications

References 12 publications

A review of uncertainty analysis in building energy assessment

A review of uncertainty analysis in building energy assessment

The relationship between quality defects and the thermal performance of buildings

Quantification of model form uncertainty in the calculation of solar diffuse irradiation on inclined surfaces for building energy simulation

Contact Info

Product

Resources

About