Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

Manfren, Massimiliano; Aste, N.; Moshksar, Reza

doi:10.1016/j.apenergy.2012.10.031

Cited by 190 publications

(97 citation statements)

References 61 publications

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“…Therefore, a key element of many optimization and Bayesian calibration approaches is the use of meta-models to carry out the inference during the calibration process, mapping the energy model's input parameters to the model's output. Examples of meta-modeling techniques that have been used to model building energy models include multiple linear regression (Li et al, 2016), support vector regression (Dong et al, 2005;Eisenhower et al, 2012b), neural networks (Neto and Fiorelli, 2008) and Gaussian Processes (Heo et al, 2012;Manfren et al, 2013).…”

Section: Automated Calibration Approachesmentioning

confidence: 99%

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Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

101

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Section: Automated Calibration Approachesmentioning

confidence: 99%

“…Using this formulation, Manfren et al (2013) calibrated a Gaussian process meta-model that has been trained on simulation data. The single model error formulation was also used to apply a Bayesian approach in the calibration of an EnergyPlus boiler and chiller model (Chong and Lam, 2015).…”

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

101

View full text Add to dashboard Cite

“…Different meta-models were applied to reduce simulation time in many studies: Multiple linear regression model (Zhao 2012;Tian et al 2014;Manfren et al 2013;Tian and Choudhary 2012), Gaussian process emulator (Manfren et al 2013;Heo 2011;Heo et al 2012;Booth et al 2012) and Support Vector Machines (Eisenhower et al 2012b). Wei et al (2015) investigated the predictive performance of six meta-models (full linear, Lasso, MARS, SVM, bagging MARS, and boosting) developed based on measured data.…”

Section: Computational Timementioning

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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“…These tools are widely used for analyzing energy consumption and determining heat load of the buildings [21][22][23][24][25][26]. Boyano et al [25] studied energy demand and potential savings in buildings based on simulation results.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Turhan

Kazanasmaz

Uygun

et al. 2014

Energy and Buildings

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a b s t r a c tThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.

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Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

Cited by 190 publications

References 61 publications

Bayesian calibration of building energy models with large datasets

Bayesian calibration of building energy models with large datasets

Review on stochastic modeling methods for building stock energy prediction

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

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