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Research and Accuracy Analysis on Engineering Quantity Calculation of a Projected Building by BIM Software Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Approximate Bayesian Computation in Parameter Estimation of Building Energy Models

Authors : ChuanQi Zhu, Wei Tian, Pieter de Wilde, Baoquan Yin

Published in: Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Publisher: Springer Singapore

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Abstract

Model calibration is a necessary step to create reliable energy models in building retrofit. Bayesian computation in model calibration has attracted more attention because it can make full use of prior knowledge on building parameters. However, the likelihood function is hard to be computed in Bayesian computation due to the complexity of building energy simulation models. Approximate Bayesian computation (ABC) is a likelihood-free method to infer unknown parameters in complicated computational models by approximating the likelihood function with simulation. The ABC method is inherently computationally intensive since a large number of simulation runs are required to find reliable inferred values. This paper proposes a method for combining the ABC technique and the machine-learning method to compute unknown parameters in parameter estimation of building energy models. The results show that this method can provide reliable estimations of unknown parameters when calibrating building energy models.

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previous chapter Parametric Building Energy Modeling Based on Engineering Method and Data Monitoring
next chapter Sensitivity Analysis of Building Energy Performance Assessment Based on Machine-Learning Models
Literature
1.
Chong, A., Menberg, K.: Guidelines for the Bayesian calibration of building energy models. Energy Build. 174, 527–547 (2018)CrossRef
2.
Tian, W., Heo, Y., de Wilde, P.: A review of uncertainty analysis in building energy assessment. Renew. Sustain. Energy Rev. 93, 285–301 (2018)CrossRef
3.
Coakley, D.: A review of methods to match building energy simulation models to measured data. Renew. Sustain. Energy Rev. 37, 123–141 (2014)CrossRef
4.
Chaudhary, G., New, J., Sanyal, J., Im, P.: Evaluation of “Autotune” calibration against manual calibration of building energy models. Appl. Energy 182, 115–134 (2016)CrossRef
5.
Lim, H., Zhai, Z.: Influences of energy data on Bayesian calibration of building energy model. Appl. Energy 231, 686–698 (2018)CrossRef
6.
Bureau, C.M.: China Standard Weather Data for Analyzing Building Thermal Conditions. China Building Industry Publishing House Beijing, China (2005)
7.
MOC.: Design Standard for Energy Efficiency of Public Buildings, China Architecture and Building Press (2015)
8.
Kuhn, M., Johnson, K.: Applied Predictive Modeling, Springer (2013)
9.
Tian, W.: A review of sensitivity analysis methods in building energy analysis. Renew. Sustain. Energy Rev. 20, 411–419 (2013)CrossRef
10.
Guideline, A.: Guideline 14-2014, Measurement of Energy, Demand, and Water Savings (2014)
11.
Tian, W., et al.: Identifying informative energy data in Bayesian calibration of building energy models. Energy Build. 119, 363–376 (2016)CrossRef
12.
Sisson, S.A., Fan, Y., Beaumont, M.: Handbook of Approximate Bayesian Computation, Chapman and Hall/CRC (2018)
13.
Sunnåker, M., Busetto, A.G., Numminen, E., Corander, J., Foll, M.: Approximate Bayesian computation. PLoS Comput. Biol. 9(1), e1002803 (2013)MathSciNetCrossRef
14.
Blum, M.G.B., François, O.: Non-linear regression models for approximate Bayesian computation. Stat. Comput. 20(1), 63–73 (2010)MathSciNetCrossRef
15.
Csilléry, K., François, O., Blum, M.G.: abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3(3), 475–479 (2012)CrossRef
Metadata
Title
Approximate Bayesian Computation in Parameter Estimation of Building Energy Models
Authors
ChuanQi Zhu
Wei Tian
Pieter de Wilde
Baoquan Yin
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Print ISBN: 978-981-13-9527-7

Electronic ISBN: 978-981-13-9528-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-9528-4_40