Bayesian-Emulator based parameter identification for calibrating energy models for existing buildings

Kang, Yoonsuk; Krarti, Moncef

doi:10.1007/s12273-016-0291-6

Cited by 22 publications

(7 citation statements)

References 17 publications

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“…Bayesian techniques have been increasingly used in estimating unknown parameters in building energy models [169][170][171][172][173]. The key feature of Bayesian methods is that expert knowledge can be incorporated with measurements into the model calibration process.…”

Section: Bayesian Techniquesmentioning

confidence: 99%

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

341

122

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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.

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Section: Bayesian Techniquesmentioning

confidence: 99%

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

341

122

View full text Add to dashboard Cite

show abstract

“…It should be borne in mind that Bayesian calibration for highly accurate energy building models can be a time-consuming procedure, with significant computational costs, especially for large and high-definition spaces. As a result, monthly calibration is usually performed and studies tend to focus on energy consumption and demand assessment: Kang and Krarti [85] apply Bayesian methods to calibrate electricity and gas consumption, considering a model resolution of 12 months (12 data points with monthly average values); Sokol, Cerezo and Reinhart [86] use the same resolution taking into account the residential building stock as the calibration target for monthly and annual energy consumption; Nagpal, Mueller, Aijazi and Reinhart [87] calibrate electricity and chilled water usage for a 12-point resolution building model; or Yuan, Nian and Su [88] evaluates Bayesian posterior distributions of an office building model using average monthly electricity consumption. Few studies conducted to date, use hourly training periods.…”

Section: Discussion and Comparison To Other Studiesmentioning

confidence: 99%

Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring

Calama-González

Symonds²,

Petrou

et al. 2021

Applied Energy

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“…Another approach is through a calibration process to refine UBEM inputs, so the simulated results match collected data. In the existing literature, Bayesian calibration is a commonly used approach for probabilistic calibration [23,24,25,26,27,28]. Overall, occupant-centric urban data can provide more information to address the more complex and multi-faceted behaviours and uncertainties in UBEMs, which are typically not captured in modelling outcome and input parameters.…”

Section: Simulation-based Building Energy Modelling At Urban Scalementioning

confidence: 99%

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim

Dong

Ouf

et al. 2020

Building and Environment

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The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

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Bayesian-Emulator based parameter identification for calibrating energy models for existing buildings

Cited by 22 publications

References 17 publications

A review of uncertainty analysis in building energy assessment

A review of uncertainty analysis in building energy assessment

Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

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