Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

Heo, Yeonsook; Augenbroe, Godfried; Graziano, Diane J.; Muehleisen, Ralph T.; Guzowski, Leah

doi:10.1016/j.buildenv.2014.12.016

Cited by 72 publications

(39 citation statements)

References 20 publications

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“…The Bayesian calibration procedure has been used in the individual building analysis for calibration of unknown input (Heo et al 2013(Heo et al , 2015aLi et al 2015aLi et al , 2016Kang and Krarti 2016;Berger et al 2016), retrofit analysis (Heo et al 2013(Heo et al , 2015a, comparison with traditional calibration method (Pavlak et al 2013), use of simplified model (Kim et al 2013;Pavlak et al 2013), influence of uncertainties in the input data (Heo et al 2015b), determination of informative energy data (Tian et al 2016), and meta-model comparison (Kim 2016;Li et al 2016). Fig.…”

Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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“…The same approach was also applied to a large portfolio of buildings (Heo et al, 2015a). To extend the method proposed by Heo et al (2012) to more complex dynamic building energy models, Li et al (2016) utilized a multiple linear regression emulator instead of a GP emulator, reducing computation cost with a slight loss in accuracy.…”

Section: Bayesian Approach To Calibrationmentioning

confidence: 99%

Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

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“…The renovation of existing buildings has attracted increasing attention globally, as retrofitting enhanced building energy efficiency, reduced energy consumption and CO 2 emissions [22][23][24], but optimal strategies should be identified in building renovations [25,26]. An adequate window glazing system is one of the most important retrofit strategies for effective energy conservation of a building, as the U-value and solar heat gain coefficient (SHGC) of windows have great impact on the heating and cooling demands of buildings [8].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Window Retrofit for High-Rise Residential Buildings in Different Climatic Zones of China

Hossain

et al. 2019

Sustainability

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The building envelope plays a significant role in the energy performance of buildings and windows are a key element in transmitting heating and cooling between the indoor and outdoor environment, and hence an adequate window system is one of the most important retrofit strategies of existing buildings for energy conservation. Therefore, this study presents a method with a theoretical case study to examine the improvement of energy efficiency in a typical high-rise residential building through window retrofitting. A building energy design model in Designbuilder along with a building information modeling (BIM) model in Revit are developed, with 20 common potential glazing alternatives being analyzed to predict the potential energy savings in the same case building with identical orientation located in a variety of climate zones in China. Based on different parameters and considerations, the results demonstrated that the currently relatively expensive low-e window glazing has the best energy performance in all climate zones, but is sufficiently close to conventionally glazed windows in its energy efficiency to discourage its adoption at present, and that, instead, a single dark conventional glazed window is preferred in a hot summer/warm winter climate, double dark traditional glazing in a hot summer/cold winter climate, and a double clear conventional window in a cold climate. Based on the simulated results, an indicative suggestion was provided to select an adequate window system for residential building retrofitting in the studied climates or similar climatic regions.

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Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

Cited by 72 publications

References 20 publications

Review on stochastic modeling methods for building stock energy prediction

Review on stochastic modeling methods for building stock energy prediction

Bayesian calibration of building energy models with large datasets

Energy-Efficient Window Retrofit for High-Rise Residential Buildings in Different Climatic Zones of China

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