Identifying informative energy data in Bayesian calibration of building energy models

Tian, Wenxi; Song, Yang; Li, Zhanyong; Wei, Shen; Pan, Wei; Liu, Yunliang

doi:10.1016/j.enbuild.2016.03.042

Cited by 90 publications

(29 citation statements)

References 28 publications

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“…To run the MCMC method, the Metropolis-Hastings algorithm [29,30] was used, which is a popular sampling technique for obtaining a sequence of random samples from joint multivariate distributions. The previous researches have showed the effectiveness of the MCMC method, in calculating the reliable posterior distributions of their Bayesian calibration, with reasonable iterations and a period of burn-in [15][16][17]. In this study, the number of iterations was defined as 5,000 with the burn-in ) (…”

Section: Statistical Calibration Using Bayesian Mcmc (Bayesian Calibrmentioning

confidence: 99%

“…For a model calibration in the building sector, the deterministic approach uses an optimization process to derive optimal values of unknown parameters by minimizing the objective function consisting of differences between measured and calculated values [11][12][13]. The statistical approach (Bayesian calibration) calculates the probability density functions of the estimated unknown parameters, reducing the difference between simulated and observed data by considering the inherently stochastic nature of input variables [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

Yoon

2017

Building and Environment

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Systematic and random errors of working sensors in building systems could significantly compromise the system's performance and thus indoor environmental quality. An extended virtual in-situ calibration has been suggested to solve problems regarding sensor errors and calibration. This calibration can correct these errors for all critical working sensors in building systems without removing working sensors or adding reference sensors as is done in a conventional calibration. This method is capable of estimating measurands using a parameter estimation technique based on mathematical system models. Deterministic and statistical methods can be used for conducting the estimation. In this study, genetic algorithm (GA)-based optimization is used as a deterministic method and Bayesian Markov Chain Monte Carlo (MCMC) is used as a statistical method to solve the calibration problem formulated by the extended virtual in-situ calibration. A case study of a single-effect LiBr-H 2 O refrigeration system illustrates the problem formulating process and compares the accuracy distributions of calibrations derived from the two different methods.A virtual in-situ sensor calibration (VIC) [8] method has been recently studied in order to solve the practical problems of a conventional sensor calibration in building energy systems; the conventional calibration process and its limitations in building systems were described in [8]. Specifically, the problems addressed are: (1) time and monetary cost; (2) disruption of normal operation; (3) difficulty in accessing various sensors embedded in equipment; and (4) the large quantity of sensors [8,9]. Meanwhile,

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Section: Statistical Calibration Using Bayesian Mcmc (Bayesian Calibrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

Yoon

2017

Building and Environment

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“…Developed by Kennedy and O'Hagan [18], the technique has been applied in a number of studies [19][20][21][22][23]. Uncertainties within a model are defined into three categories: model output uncertainty, discrepancy between model outputs and measured data, and the uncertainty of measured data.…”

Section: Model Calibrationmentioning

confidence: 99%

Variations in the U-Value Measurement of a Whole Dwelling Using Infrared Thermography under Controlled Conditions

et al. 2018

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U-values of building elements are often determined using point measurements, where infrared imagery may be used to identify a suitable location for these measurements. Current methods identify that surface areas exhibiting a homogeneous temperature-away from regions of thermal bridging-can be used to obtain U-values. In doing so, however, the resulting U-value is assumed to represent that entire building element, contrary to the information given by the initial infrared inspection. This can be problematic when applying these measured U-values to models for predicting energy performance. Three techniques have been used to measure the U-values of external building elements of a full-scale replica of a pre-1920s U.K. home under controlled conditions: point measurements, using heat flux meters, and two variations of infrared thermography at high and low resolutions. U-values determined from each technique were used to calibrate a model of that building and predictions of the heat transfer coefficient, annual energy consumption, and fuel cost were made. Point measurements and low-resolution infrared thermography were found to represent a relatively small proportion of the overall U-value distribution. By propagating the variation of U-values found using high-resolution thermography, the predicted heat transfer coefficient (HTC) was found to vary between 183 W/K to 235 W/K (±12%). This also led to subsequent variations in the predictions for annual energy consumption for heating (between 4923 kWh and 5481 kWh, ±11%); and in the predicted cost of that energy consumption (between £227 and £281, ±24%). This variation is indicative of the sensitivity of energy simulations to sensor placement when carrying out point measurements for U-values.

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“…It is necessary to study whether the model can be calibrated with insufficient energy use data. Tian et al (2016) investigated how to determine informative energy data in Bayesian calibration using correlation analysis and a hierarchical clustering method. This method can improve understanding of the amount and quality of energy usage data required for Bayesian calibration.…”

Section: Energy Use Data For Calibrationmentioning

confidence: 99%

“…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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Identifying informative energy data in Bayesian calibration of building energy models

Cited by 90 publications

References 28 publications

A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

A quantitative comparison of statistical and deterministic methods on virtual in-situ calibration in building systems

Variations in the U-Value Measurement of a Whole Dwelling Using Infrared Thermography under Controlled Conditions

Review on stochastic modeling methods for building stock energy prediction

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