IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v215y2018icp31-40.html
   My bibliography  Save this article

Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect

Author

Listed:
  • Santos, Luis Guilherme Resende
  • Afshari, Afshin
  • Norford, Leslie K.
  • Mao, Jiachen

Abstract

Over the past decade, the building energy research community has increasingly focused on urban-scale models. The shortcomings of analyzing isolated buildings in an urban context are well-known and far from negligible, mainly due to the inability to account for Urban Heat Island (UHI), shading from neighboring obstructions, and obstructed wind flow. The aim of this paper is to evaluate the impact of the urban context via urban-scale modelling and inverse parameter estimation (calibration) using metered building energy consumption. We describe an automated calibration process for modelling 56 buildings in a representative district in downtown Abu Dhabi (UAE), where a detailed energy audit was conducted with data from 2008 to 2010. Since the urban ambient air temperature could differ significantly from the reference rural air temperature used in most building simulations, the calibration procedure will also consider this UHI effect. Two main approaches of district-wide energy model calibration are proposed using a genetic algorithm and compared to a baseline case where UHI is not considered. The first approach estimates seven building-related parameters together with four microclimate-related variables (describing annual average and seasonal variation of the UHI effect on both air temperature and humidity). The second one uses the Urban Weather Generator (UWG) to pre-process the urban EPW file, thereby reducing the number of the parameters to be estimated. In addition, two approaches are investigated for the calculation of the ASHRAE Guideline 14 calibration error metrics (CvRMSE and NMBE). One approach is to look at the whole district as one aggregate building, while the other, introduced for the first time herein, consists in deriving the weighted-average of the error of each building. The main contribution of this study is to provide simultaneous calibration for multiple buildings in the same district—and subject to the same UHI intensity. Hence, the UHI intensity (urban-rural temperature/humidity differential) is estimated alongside other calibration parameters. For the weighted average approach, CvRMSE found is between 19.09% and 19.40%, while NMBE is between 16.24% and 16.39%. For the aggregated building, CvRMSE is between 2.71% and 4.04%, while NMBE is between 1.95% and 2.35%.

Suggested Citation

  • Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
    • Handle: RePEc:eee:appene:v:215:y:2018:i:c:p:31-40
    DOI: 10.1016/j.apenergy.2018.01.089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918301041
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.01.089?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. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
    2. Rossi, Federico & Pisello, Anna Laura & Nicolini, Andrea & Filipponi, Mirko & Palombo, Massimo, 2014. "Analysis of retro-reflective surfaces for urban heat island mitigation: A new analytical model," Applied Energy, Elsevier, vol. 114(C), pages 621-631.
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    5. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    6. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    7. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    8. 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.
    9. 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.
    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. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    2. Martín Mosteiro-Romero & Arno Schlueter, 2021. "Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling," Energies, MDPI, vol. 14(8), pages 1-30, April.
    3. Duan, Shuangping & Luo, Zhiwen & Yang, Xinyan & Li, Yuguo, 2019. "The impact of building operations on urban heat/cool islands under urban densification: A comparison between naturally-ventilated and air-conditioned buildings," Applied Energy, Elsevier, vol. 235(C), pages 129-138.
    4. Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.

    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. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    2. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    3. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    4. Eleftheria Touloupaki & Theodoros Theodosiou, 2017. "Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review," Energies, MDPI, vol. 10(5), pages 1-18, May.
    5. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.
    6. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
    7. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
    8. Orosz, Matthew & Altes-Buch, Queralt & Mueller, Amy & Lemort, Vincent, 2018. "Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa," Applied Energy, Elsevier, vol. 218(C), pages 382-390.
    9. Mauricio Nath Lopes & Roberto Lamberts, 2018. "Development of a Metamodel to Predict Cooling Energy Consumption of HVAC Systems in Office Buildings in Different Climates," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
    10. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    11. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
    12. Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
    13. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    14. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
    15. 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.
    16. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    17. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    18. 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.
    19. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    20. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(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:215:y:2018:i:c:p:31-40. 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.