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Page de résumé pour ULgetd-03292012-113358

Auteur : Bertagnolio, Stéphane
E-mail de l'auteur : Stephane.Bertagnolio@ulg.ac.be
URN : ULgetd-03292012-113358
Langue : Anglais/English
Titre : Evidence-Based Model Calibration for Efficient Building Energy Services
Intitulé du diplôme : Doctorat en sciences de l'ingénieur
Département : FSA - Département d'aérospatiale et de mécanique
Jury :
Nom : Titre :
Hauglustaine, Jean-Marie Membre du jury/Committee Member
Kummert, Michaël Membre du jury/Committee Member
Lebrun, Jean Membre du jury/Committee Member
Lemort, Vincent Membre du jury/Committee Member
Saelens, Dirk Membre du jury/Committee Member
Stabat, Pascal Membre du jury/Committee Member
Dewallef, Pierre Président du jury/Committee Chair
André, Philippe Promoteur/Director
Mots-clés :
  • model calibration
  • simulation
  • modeling
  • energy use analysis
  • Building energy efficiency
Date de soutenance : 2012-06-22
Type d'accès : Public/Internet
Résumé :

Energy services play a growing role in the control of energy consumption and the improvement of energy efficiency in non-residential buildings. Most of the energy use analyses involved in the energy efficiency service process require on-field measurements and energy use analysis. Today, while detailed on-field measurements and energy counting stay generally expensive and time-consuming, energy simulations are increasingly cheaper due to the continuous improvement of computer speed.

This work consists in the development of a simulation-based approach dedicated to whole-building energy use analysis for use in the frame of an energy efficiency service process. Focus is given to the development of a new simplified dynamic hourly building energy simulation tool adapted to energy use analysis of existing buildings, its calibration by means of available energy use data and to the integration of the calibration process into the Energy Service Process. The proposed evidence-based calibration methodology is deeply related to on-field inspection and data collection issues and is developed to fit with the audit/inspection process. After calibration, the model can be used to support the other steps of the Energy Services Process, such as ECOs selection and evaluation and continuous performance verification.

The new systematic calibration methodology gives priority to the physical identification of the model’s parameters (i.e. to the direct measurement) and relies on the notion of hierarchy among the source of information (as a function of their reliability) used to identify the value of the parameters.

The improved Morris’ sensitivity analysis method is used for “factor fixing” (i.e. distinction between non-influential model’s parameters) and “parameters screening” (i.e. classification of influential parameters by order of importance) in order to orient the data collection work and guide the parameters adjustment process. At the end of the calibration process, the Latin Hypercube Monte Carlo sampling is used to quantify the uncertainty on the final outputs of the calibrated model.

The developed simulation tool and the associated calibration method are applied to a synthetic case (“Virtual Calibration Test Bed”) and to real case study building located in Brussels, Belgium.

Both applications (real and synthetic cases) allow highlighting the complexity and the limits of calibration as it is used today. Calibration remains a highly underdetermined problem and a compromise has to be found between data collection and modeling efforts and model’s requirements in order to proceed to efficient energy use analysis. At the end of these applications, it is believed that partially manual methods remain more efficient and the best quality assurance when proceeding to calibration of a building energy simulation model.

The use of an evidence-based method ensures sticking to reality and avoids bad representation and hazardous adjustment of the parameters. Moreover, it is shown that the intensive use of a sensitivity analysis method is of a great help to orient data collection and parameters adjustment processes. Defining confidence/uncertainty ranges for each parameter, in addition to a “best-guess” value, also allowed quantifying the uncertainty on the final outputs of the model and helped the user in evaluating the quality of the calibrated model.

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[Public/Internet] PhDSB_June2012.pdf 11.66 Mb 00:27:46 00:01:02

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