Application of parameters space analysis tools for empirical model validation

Barrio, Elena Palomo del; Guyon, Gilles

doi:10.1016/s0378-7788(03)00039-2

Cited by 15 publications

(3 citation statements)

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“…Principal component analysis (PCA) is a way to move the problem into a less correlated parameter space. It was applied by [Palomo Del Barrio and Guyon, 2003] before the calibration of a thermal model [Palomo del Barrio and Guyon, 2004]. Similarly, [Cai and Braun, 2015] use a significance vector defined as the square root of diagonal elements of the information matrix S T S, then apply a method based on principal component analysis (PCA) to remove the most correlated parameters from it.…”

Section: Practical Identifiabilitymentioning

confidence: 99%

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Rouchier

2018

Energy and Buildings

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Building physics researchers have benefitted from elements of statistical learning and time series analysis to improve their ability to construct knowledge from data. What is referred to here as inverse problems are actually a very broad field that encompasses any study where data is gathered and mined for information. The purpose of the present article is twofold. First, it is a tutorial on the formalism of inverse problems in building physics and the most common ways to solve them. Then, it provides an overview of tools and methods that can either be used to assess or the reliability of inverse problem results, prevent erroneous interpretation of data, and optimise information gained by experiments. It provides an introduction, along with useful references, to the topics of estimation error assessment, regularisation, identifiability analysis, residual analysis, model selection and optimal experiment design. These concepts are presented in the context of building simulation and energy performance assessment: a simple RC model is used as a running example to illustrate each chapter.

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Section: Practical Identifiabilitymentioning

confidence: 99%

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Rouchier

2018

Energy and Buildings

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“…Another interesting two-step methodology was proposed by Palomo del Barrio et al [89], with specific regard to the validation of empirical models. Based on the analysis of the model parameters space, the methodology first checks the model validity to detect significant disagreements between measurements and simulations in the model performance (sensitivity analysis), and then investigates the differences between model simulations and measurements (optimization of model parameters).…”

Section: Calibrated Simulation Applicationsmentioning

confidence: 99%

Methodologies and Advancements in the Calibration of Building Energy Models

2015

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Buildings do not usually perform during operation as well as predicted during the design stage. Disagreement between simulated and metered energy consumption represents a common issue in building simulation. For this reason, the calibration of building simulation models is of growing interest. Sensitivity and uncertainty analyses play an important role in building model accuracy. They can be used to identify the building model parameters most influent on the energy consumption. Given this, these analyses should be integrated within calibration methodologies and applications for tuning the parameters. This paper aims at providing a picture of the state of the art of calibration methodologies in the domain of building energy performance assessment. First, the most common methodologies for calibration are presented, emphasizing criticalities and gaps that can be faced. In particular the main issues to be addressed, when carrying out calibrated simulation, are discussed. The standard statistical criteria for considering the building models calibrated and for evaluating their goodness-of-fit are also presented. Second, the commonly used techniques for investigating uncertainties in building models are reviewed. Third, a review of the latest main studies in the calibrated simulation domain is presented. Criticalities and recommendations for new studies are finally provided. OPEN ACCESSEnergies 2015, 8 2549

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“…Sensitivity analysis is a well-established technique in computer simulations (Saltelli et al 2004(Saltelli et al , 2000Santer et al, 2003) and has been implemented in building energy simulation codes ) and empirical validations (Mara et al, 2001;Aude et al, 2000;Fürbringer andRoulet, 1999, 1995;Lomas and Eppel, 1992) for many years. A thorough methodology for sensitivity analysis for calculations, correlation analysis, principle component analysis, and implementation in the framework of empirical validations in IEA-SHC Task 22 are described by Palomo Del Barrio and Guyon (2004;. In the context of the International Energy Agency's (IEA) SHC Task 34/ ECBCS Annex 43 Subtask C, a series of empirical validations is being performed in a test cell to assess the accuracy of solar gain models in building energy simulation codes with/without shading devices and frames.…”

Section: /12mentioning

confidence: 99%

Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation

et al. 2007

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Accurately computing solar irradiance on external facades is a prerequisite for reliably predicting thermal behavior and cooling loads of buildings. Validation of radiation models and algorithms implemented in building energy simulation codes is an essential endeavor for evaluating solar gain models. Seven solar radiation models implemented in four building energy simulation codes were investigated: (1) isotropic sky, (2) Klucher, (3) Hay-Davies, (4) Reindl, (5) Muneer, (6) 1987 Perez, and (7) 1990 Perez models. The building energy simulation codes included: EnergyPlus, DOE-2.1E, TRNSYS-TUD, and ESP-r. Solar radiation data from two 25 days periods in October and March/April, which included diverse atmospheric conditions and solar altitudes, measured on the EMPA campus in a suburban area in Duebendorf, Switzerland, were used for validation purposes. Two of the three measured components of solar irradiances - global horizontal, diffuse horizontal and direct-normal - were used as inputs for calculating global irradiance on a south-west façade. Numerous statistical parameters were employed to analyze hourly measured and predicted global vertical irradiances. Mean absolute differences for both periods were found to be: (1) 13.7% and 14.9% for the isotropic sky model, (2) 9.1% for the Hay-Davies model, (3) 9.4% for the Reindl model, (4) 7.6% for the Muneer model, (5) 13.2% for the Klucher model, (6) 9.0%, 7.7%, 6.6%, and 7.1% for the 1990 Perez models, and (7) 7.9% for the 1987 Perez model. Detailed sensitivity analyses using Monte Carlo and fitted effects for N-way factorial analyses were applied to assess how uncertainties in input parameters propagated through one of the building energy simulation codes and impacted the output parameter. The implications of deviations in computed solar irradiances on predicted thermal behavior and cooling load of buildings are discussed

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Application of parameters space analysis tools for empirical model validation

Cited by 15 publications

References 1 publication

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Solving inverse problems in building physics: An overview of guidelines for a careful and optimal use of data

Methodologies and Advancements in the Calibration of Building Energy Models

Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation

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