IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v168y2016icp691-705.html
   My bibliography  Save this article

Genetic algorithm for building envelope calibration

Author

Listed:
  • Ramos Ruiz, Germán
  • Fernández Bandera, Carlos
  • Gómez-Acebo Temes, Tomás
  • Sánchez-Ostiz Gutierrez, Ana

Abstract

Buildings today represent 40% of world primary energy consumption and 24% of greenhouse gas emissions. In our society there is growing interest in knowing precisely when and how energy consumption occurs. This means that consumption measurement and verification plans are well-advanced. International agencies such as Efficiency Valuation Organization (EVO) and International Performance Measurement and Verification Protocol (IPMVP) have developed methodologies to quantify savings. This paper presents a methodology to accurately perform automated envelope calibration under option D (calibrated simulation) of IPMVP – vol. 1. This is frequently ignored because of its complexity, despite being more flexible and accurate in assessing the energy performance of a building. A detailed baseline energy model is used, and by means of a metaheuristic technique achieves a highly reliable and accurate Building Energy Simulation (BES) model suitable for detailed analysis of saving strategies. In order to find this BES model a Genetic Algorithm (NSGA-II) is used, together with a highly efficient engine to stimulate the objective, thus permitting rapid achievement of the goal. The result is a BES model that broadly captures the heat dynamic behaviour of the building. The model amply fulfils the parameters demanded by ASHRAE and EVO under option D.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:168:y:2016:i:c:p:691-705
    DOI: 10.1016/j.apenergy.2016.01.075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916300629
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.01.075?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. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    2. Robertson, Joseph J. & Polly, Ben J. & Collis, Jon M., 2015. "Reduced-order modeling and simulated annealing optimization for efficient residential building utility bill calibration," Applied Energy, Elsevier, vol. 148(C), pages 169-177.
    3. Mechri, Houcem Eddine & Capozzoli, Alfonso & Corrado, Vincenzo, 2010. "USE of the ANOVA approach for sensitive building energy design," Applied Energy, Elsevier, vol. 87(10), pages 3073-3083, October.
    4. Chantrelle, Fanny Pernodet & Lahmidi, Hicham & Keilholz, Werner & Mankibi, Mohamed El & Michel, Pierre, 2011. "Development of a multicriteria tool for optimizing the renovation of buildings," Applied Energy, Elsevier, vol. 88(4), pages 1386-1394, April.
    5. 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.
    6. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    7. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
    8. 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.
    9. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    10. Heiselberg, Per & Brohus, Henrik & Hesselholt, Allan & Rasmussen, Henrik & Seinre, Erkki & Thomas, Sara, 2009. "Application of sensitivity analysis in design of sustainable buildings," Renewable Energy, Elsevier, vol. 34(9), pages 2030-2036.
    11. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    12. 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.
    13. 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.
    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. 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. 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.
    3. 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.
    4. Carlos Fernández Bandera & Germán Ramos Ruiz, 2017. "Towards a New Generation of Building Envelope Calibration," Energies, MDPI, vol. 10(12), pages 1-19, December.
    5. 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.
    6. 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.
    7. 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.
    8. Chen, Xi & Yang, Hongxing & Sun, Ke, 2016. "A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong," Energy, Elsevier, vol. 113(C), pages 267-281.
    9. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    10. Echarri-Iribarren, Victor & Echarri-Iribarren, Fernando & Rizo-Maestre, Carlos, 2019. "Ceramic panels versus aluminium in buildings: Energy consumption and environmental impact assessment with a new methodology," Applied Energy, Elsevier, vol. 233, pages 959-974.
    11. 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.
    12. Carlos Fernández Bandera & Ana Fei Muñoz Mardones & Hu Du & Juan Echevarría Trueba & Germán Ramos Ruiz, 2018. "Exergy As a Measure of Sustainable Retrofitting of Buildings," Energies, MDPI, vol. 11(11), pages 1-19, November.
    13. Chen, Hanfei & Lin, ChihChieh & Longtin, Jon P., 2019. "Dynamic modeling and parameter optimization of a free-piston Vuilleumier heat pump with dwell-based motion," Applied Energy, Elsevier, vol. 242(C), pages 741-751.
    14. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    15. 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.
    16. 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.
    17. Yunyan Li & Yuansheng Huang & Meimei Zhang, 2018. "Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network," Energies, MDPI, vol. 11(5), pages 1-18, May.
    18. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    19. Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.
    20. 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.
    21. 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.
    22. 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).
    23. 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.
    24. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.

    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, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
    2. 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.
    3. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    10. 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.
    11. 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.
    12. 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.
    13. Cristina Brunelli & Francesco Castellani & Alberto Garinei & Lorenzo Biondi & Marcello Marconi, 2016. "A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings," Energies, MDPI, vol. 9(11), pages 1-15, November.
    14. Nayara R. M. Sakiyama & Joyce C. Carlo & Leonardo Mazzaferro & Harald Garrecht, 2021. "Building Optimization through a Parametric Design Platform: Using Sensitivity Analysis to Improve a Radial-Based Algorithm Performance," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    15. 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.
    16. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    17. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    18. Shi, Xing & Tian, Zhichao & Chen, Wenqiang & Si, Binghui & Jin, Xing, 2016. "A review on building energy efficient design optimization rom the perspective of architects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 872-884.
    19. Aste, Niccolò & Leonforte, Fabrizio & Manfren, Massimiliano & Mazzon, Manlio, 2015. "Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study," Applied Energy, Elsevier, vol. 145(C), pages 111-123.
    20. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.

    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:168:y:2016:i:c:p:691-705. 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.