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Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

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  • Vicente Gutiérrez González

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

  • Germán Ramos Ruiz

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

  • Carlos Fernández Bandera

    (School of Architecture, University of Navarra, 31009 Pamplona, Spain)

Abstract

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient ( R 2 ). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

Suggested Citation

  • Vicente Gutiérrez González & Germán Ramos Ruiz & Carlos Fernández Bandera, 2021. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data," Energies, MDPI, vol. 14(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1187-:d:504083
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    References listed on IDEAS

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    1. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    2. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.
    3. Bloess, Andreas & Schill, Wolf-Peter & Zerrahn, Alexander, 2018. "Power-to-heat for renewable energy integration: A review of technologies, modeling approaches, and flexibility potentials," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 212, pages 1611-1626.
    4. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    5. Vicente Gutiérrez González & Lissette Álvarez Colmenares & Jesús Fernando López Fidalgo & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models," Energies, MDPI, vol. 12(11), pages 1-18, May.
    6. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    7. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
    8. Carlos Fernández Bandera & Jose Pachano & Jaume Salom & Antonis Peppas & Germán Ramos Ruiz, 2020. "Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
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