IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v250y2019icp991-1010.html
   My bibliography  Save this article

Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches

Author

Listed:
  • O' Donovan, Adam
  • O' Sullivan, Paul D.
  • Murphy, Michael D.

Abstract

As the cooling energy demand in buildings is set to increase dramatically in the future, the exploitation of passive solutions like natural ventilation could prove vital in reducing the reliance on mechanical systems. Models that can predict air temperature accurately in naturally ventilated mode are key to understanding the potential of natural ventilation now and in the future. This article presents a simulation based case study of a retrofitted nearly zero energy test-bed university building, in naturally ventilated mode only. The study had three aims: (1) calibration and validation of a whole building energy model, (2) a comparative analysis of occupancy schedules and opening control strategies, and (3) a comparison of researcher and practitioner approaches. Results showed the detailed building model was capable of predicting room level air temperature with a low level of error (0.27 °C ≤ RMSE ≤ 1.50 °C) that was well within the limits of existing calibration standards (MBE ±10%, CVRMSE <20%). The comparative analysis highlighted the need to consider occupancy schedules that have a wide range of diversity, and opening control strategies that reflect the manual and automated relationship in natural ventilation systems. The approach comparison highlighted that both practitioner and researcher approaches to simulating both occupancy schedules and opening control strategies showed similar levels of performance for the application considered. The paper also provides recommendations for those modelling air temperatures and thermal comfort in nearly zero energy buildings.

Suggested Citation

  • O' Donovan, Adam & O' Sullivan, Paul D. & Murphy, Michael D., 2019. "Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches," Applied Energy, Elsevier, vol. 250(C), pages 991-1010.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:991-1010
    DOI: 10.1016/j.apenergy.2019.04.082
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919307457
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.04.082?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oropeza-Perez, Ivan & Østergaard, Poul Alberg, 2014. "Potential of natural ventilation in temperate countries – A case study of Denmark," Applied Energy, Elsevier, vol. 114(C), pages 520-530.
    2. 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.
    3. Oropeza-Perez, Ivan & Østergaard, Poul Alberg, 2014. "Energy saving potential of utilizing natural ventilation under warm conditions – A case study of Mexico," Applied Energy, Elsevier, vol. 130(C), pages 20-32.
    4. Ormandy, David & Ezratty, Véronique, 2012. "Health and thermal comfort: From WHO guidance to housing strategies," Energy Policy, Elsevier, vol. 49(C), pages 116-121.
    5. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    6. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    7. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    8. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    9. Calautit, John Kaiser & Hughes, Ben Richard & Nasir, Diana SNM, 2017. "Climatic analysis of a passive cooling technology for the built environment in hot countries," Applied Energy, Elsevier, vol. 186(P3), pages 321-335.
    10. Mustafaraj, Giorgio & Marini, Dashamir & Costa, Andrea & Keane, Marcus, 2014. "Model calibration for building energy efficiency simulation," Applied Energy, Elsevier, vol. 130(C), pages 72-85.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pengying Wang & Shuo Zhang, 2022. "Retrofitting Strategies Based on Orthogonal Array Testing to Develop Nearly Zero Energy Buildings," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    2. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
    3. Yishao Shi & Danxuan Liu, 2020. "Relationship between Urban New Business Indexes and the Business Environment of Chinese Cities: A Study Based on Entropy-TOPSIS and a Gaussian Process Regression Model," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    4. Roya Aeinehvand & Amiraslan Darvish & Abdollah Baghaei Daemei & Shima Barati & Asma Jamali & Vahid Malekpour Ravasjan, 2021. "Proposing Alternative Solutions to Enhance Natural Ventilation Rates in Residential Buildings in the Cfa Climate Zone of Rasht," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    5. Alexander Brem & Ken Bruton & Paul D. O’Sullivan, 2021. "Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response," Energies, MDPI, vol. 14(19), pages 1-28, October.
    6. Rosa Francesca De Masi & Antonio Gigante & Valentino Festa & Silvia Ruggiero & Giuseppe Peter Vanoli, 2021. "Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB," Energies, MDPI, vol. 14(21), pages 1-30, November.
    7. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
    8. Tadeusz Kuczyński & Anna Staszczuk, 2023. "Impact of Uninsulated Slab-on-Grade and Masonry Walls on Residential Building Overheating," Energies, MDPI, vol. 16(22), pages 1-22, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    3. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    4. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
    5. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    6. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    7. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    8. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    9. Martins, Nuno R. & Carrilho da Graça, Guilherme, 2017. "Impact of outdoor PM2.5 on natural ventilation usability in California’s nondomestic buildings," Applied Energy, Elsevier, vol. 189(C), pages 711-724.
    10. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.
    11. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
    12. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    13. Carlos Fernández Bandera & Germán Ramos Ruiz, 2017. "Towards a New Generation of Building Envelope Calibration," Energies, MDPI, vol. 10(12), pages 1-19, December.
    14. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    15. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.
    16. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    17. Calama-González, Carmen María & Symonds, Phil & Petrou, Giorgos & Suárez, Rafael & León-Rodríguez, Ángel Luis, 2021. "Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring," Applied Energy, Elsevier, vol. 282(PA).
    18. Sukjoon Oh & Juan-Carlos Baltazar & Jeff S. Haberl, 2022. "Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    19. Hiyama, Kyosuke & Glicksman, Leon, 2015. "Preliminary design method for naturally ventilated buildings using target air change rate and natural ventilation potential maps in the United States," Energy, Elsevier, vol. 89(C), pages 655-666.
    20. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:250:y:2019:i:c:p:991-1010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.