A methodology for meta-model based optimization in building energy models

Eisenhower, Bryan; O’Neill, Zheng; Narayanan, S.; Fonoberov, Vladimir A.; Mezić, Igor

doi:10.1016/j.enbuild.2011.12.001

Cited by 226 publications

(136 citation statements)

References 19 publications

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“…Output convergence and sampling efficiency have been studied, resulting in quasi-Monte Carlo sampling schemes instead of random sampling as generally described by Janssen [6] and illustrated by Burhenne et al [7]. Furthermore, Eisenhower et al [8] introduced the use of meta-models into building energy optimisation. Meta-modelling allows replacing a time-inefficient model by a model with a highly reduced calculation time.…”

Section: Monte-carlo-based Techniquesmentioning

confidence: 99%

“…As the proposed methodology may rapidly result in a high computational cost, even for very time-efficient models as those in referred literature, it is preferable to replace the original model with a simpler and much faster metamodel [8]. Furthermore, both convergence and sampling efficiency are crucial to overcome time issues while obtaining reliable results [6], as well as sensitivity analysis to select and determine input parameter distributions based on measurements or expertise [10].…”

Section: Paper Objectivesmentioning

confidence: 99%

“…Meta-models, also known as surrogate models, aim at mimicking the original simulation model, but with a highly reduced calculation time, which facilitates the optimisation procedure [8]. To construct such a meta-model, several simulations are run with the original tool.…”

Section: Meta-modelmentioning

confidence: 99%

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Probabilistic design and analysis of building performances: Methodology and application example

Gelder

Janssen

Roels

2014

Energy and Buildings

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Both building performance analysis and multi-criteria performance optimisation often use deterministic simulations. Since many influencing parameters are generally inherently uncertain, this may lead to unreliable predictions of design impact. Therefore, this paper proposes a probabilistic analysis and design method to incorporate these uncertainties. The embedded Monte Carlo based uncertainty and sensitivity analyses investigate the output distributions. To greatly reduce computational efforts, meta-models can be incorporated, replacing the original model. Additionally, multi-layered sampling schemes are used to subject all design options to the same uncertainties and to check the validity of optimisation results for potential scenarios. Since reliability is a key aspect in this methodology, the paper also focuses on output convergence and method reliability.To optimise both average performances and spread, effectiveness ε and robustness R P are introduced as output uncertainty indicators, inspired by robust design. Here, effectiveness is defined as the ability of the design option to optimise the performance, while robustness is defined as the ability to stabilise this performance for the entire range of input uncertainties.The successive methodology steps are explained using a simplified application example.

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Section: Monte-carlo-based Techniquesmentioning

confidence: 99%

Section: Paper Objectivesmentioning

confidence: 99%

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Probabilistic design and analysis of building performances: Methodology and application example

Gelder

Janssen

Roels

2014

Energy and Buildings

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“…A meta-model (also called surrogate model) can be defined as a "model of model" (Eisenhower et al 2012b), which is simpler and computationally faster than an original model. 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).…”

Section: Computational Timementioning

confidence: 99%

“…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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2018

Building Performance Analysis

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A methodology for meta-model based optimization in building energy models

Cited by 226 publications

References 19 publications

Probabilistic design and analysis of building performances: Methodology and application example

Probabilistic design and analysis of building performances: Methodology and application example

Review on stochastic modeling methods for building stock energy prediction

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