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Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain

Author

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  • Ana Ogando

    (Department of Mechanical Engineering, Heat Engines and Fluid Mechanics, School of Industrial Engineering, University of Vigo, Vigo 36310, Spain)

  • Natalia Cid

    (Department of Mechanical Engineering, Heat Engines and Fluid Mechanics, School of Industrial Engineering, University of Vigo, Vigo 36310, Spain)

  • Marta Fernández

    (Department of Mechanical Engineering, Heat Engines and Fluid Mechanics, School of Industrial Engineering, University of Vigo, Vigo 36310, Spain)

Abstract

In the present paper, the energy performance of buildings forming a school centre in the northwest of Spain was analyzed using a transient simulation of the energy model of the school, which was developed with TRNSYS, a software of proven reliability in the field of thermal simulations. A deterministic calibration approach was applied to the initial building model to adjust the predictions to the actual performance of the school, data acquired during the temperature measurement campaign. The buildings under study were in deteriorated conditions due to poor maintenance over the years, presenting a big challenge for modelling and simulating it in a reliable way. The results showed that the proposed methodology is successful for obtaining calibrated thermal models of these types of damaged buildings, as the metrics employed to verify the final error showed a reduced normalized mean bias error (NMBE) of 2.73%. It was verified that a decrease of approximately 60% in NMBE and 17% in the coefficient of variation of the root mean square error (CV(RMSE)) was achieved due to the calibration process. Subsequent steps were performed with the aid of new software, which was developed under a European project that enabled the automated calibration of the simulations.

Suggested Citation

  • Ana Ogando & Natalia Cid & Marta Fernández, 2017. "Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain," Energies, MDPI, vol. 10(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:807-:d:101466
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    References listed on IDEAS

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    1. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    2. Anna Laura Pisello & Michael Bobker & Franco Cotana, 2012. "A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy," Energies, MDPI, vol. 5(12), pages 1-22, December.
    3. Guillermo Rey & Carlos Ulloa & José Luís Míguez & Antón Cacabelos, 2016. "Suitability Assessment of an ICE-Based Micro-CCHP Unit in Different Spanish Climatic Zones: Application of an Experimental Model in Transient Simulation," Energies, MDPI, vol. 9(11), pages 1-13, November.
    4. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    5. Young Tae Chae & Young M. Lee & David Longinott, 2016. "Assessment of Retrofitting Measures for a Large Historic Research Facility Using a Building Energy Simulation Model," Energies, MDPI, vol. 9(6), pages 1-18, June.
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    Cited by:

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