Leveraging the analysis of parametric uncertainty for building energy model calibration

O’Neill, Zheng; Eisenhower, Bryan

doi:10.1007/s12273-013-0125-8

Cited by 67 publications

(33 citation statements)

References 13 publications

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“…Real data of hourly and monthly power, energy and temperature were used to calibrate the lighting, equipment and HVAC system modelled in the Energy Plus tool. O'Neill and Eisenhower [8] defined a methodology to assess the most important parameters affecting the simulation in order to identify parameter combinations that produce the best fit to measured data. Raftery et al [9] used a case study with high-level of detail to demonstrate a methodology for calibrating whole building energy models by using measured lighting and plug load data in the simulation at hourly intervals.…”

Section: Introductionmentioning

confidence: 99%

Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings

Andersen

Fabi

Corgnati

2016

Energy and Buildings

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Section: Introductionmentioning

confidence: 99%

Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings

Andersen

Fabi

Corgnati

2016

Energy and Buildings

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“…Disagreement between simulated and metered energy consumption represents a common issue in building simulation [16]. When dealing with existing buildings, models are usually calibrated in order to obtain low values of the Coefficient of Variation of Root Mean Squared Error (CV (RMSE)) and of Normalized Mean Bias Error (NMBE) for the whole building's monthly use [17,18]. Those standard metrics are usually applied to compare the outputs from the calibrated model with observed values of energy consumption:…”

Section: Uncertainty Evaluationmentioning

confidence: 99%

A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings

et al. 2016

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When dealing with sustainable design concepts in new construction or in retrofitting existing buildings, it is useful to define both economic and environmental performance indicators, in order to select the optimal technical solutions. In most of the cases, the definition of the optimal strategy is not trivial because it is necessary to solve a multi-objective problem with a high number of the variables subjected to nonlinear constraints. In this study, a powerful multi-objective optimization genetic algorithm, NSGAII (Non-dominated Sorting Genetic Algorithm-II), is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationship between objectives. A method is then proposed, to introduce uncertainty evaluation in the optimization procedure. A new university building is taken as a case study to demonstrate how each step of the optimization process should be performed. The results achieved turn out to be reliable and show the suitableness of this procedure to define both economic and environmental performance indicators. Similar analysis on a set of buildings representatives of a specific region might be used to assist local/national administrations in the definition of appropriate legal limits that will permit a strategic optimized extension of renewable energy production. Finally, the proposed approach could be applied to similar optimization models for the optimal planning of sustainable buildings, in order to define the best solutions among non-optimal ones.

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“…Several studies based on calibration have been carried out [6,[12][13][14][15][16][17] but as yet no universal consensus guidelines have been presented. There are thus standard criteria for validating a calibrated model but the lack of a formal and recognized methodology still makes CS a process highly dependent on the user's skills and judgments.…”

Section: Introductionmentioning

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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Leveraging the analysis of parametric uncertainty for building energy model calibration

Cited by 67 publications

References 13 publications

Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings

Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings

A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings

Methodologies and Advancements in the Calibration of Building Energy Models

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