IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v139y2015icp230-244.html
   My bibliography  Save this article

Order selection of thermal models by frequency analysis of measurements for building energy efficiency estimation

Author

Listed:
  • Naveros, I.
  • Ghiaus, C.

Abstract

Experimental identification of the dynamic models of heat transfer in walls is needed for optimal control and characterization of building energy performance. These models use the heat equation in time domain which can be put in matrix form and then, through state-space representation, transformed in a transfer function which is of infinite order. However, the model acts as a low-pass filter and needs to respond only to the frequency spectrum present in the measured inputs. Then, the order of the transfer function can be determined by using the frequency spectrum of the measured inputs and the accuracy of the sensors. The main idea is that from two models of different orders, the one with a lower order can be used in building parameter identification, when the difference between the outputs is negligible or lower than the output measurement error. A homogeneous light wall is used as an example for a detailed study and examples of homogeneous building elements with very high and very low time constants are given. The first order model is compared with a very high order model (hundreds of states) which can be considered almost continuous in space.

Suggested Citation

  • Naveros, I. & Ghiaus, C., 2015. "Order selection of thermal models by frequency analysis of measurements for building energy efficiency estimation," Applied Energy, Elsevier, vol. 139(C), pages 230-244.
    • Handle: RePEc:eee:appene:v:139:y:2015:i:c:p:230-244
    DOI: 10.1016/j.apenergy.2014.11.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914011957
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2014.11.033?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. Xu, Xiaoqi & Culligan, Patricia J. & Taylor, John E., 2014. "Energy Saving Alignment Strategy: Achieving energy efficiency in urban buildings by matching occupant temperature preferences with a building’s indoor thermal environment," Applied Energy, Elsevier, vol. 123(C), pages 209-219.
    2. Stadler, M. & Groissböck, M. & Cardoso, G. & Marnay, C., 2014. "Optimizing Distributed Energy Resources and building retrofits with the strategic DER-CAModel," Applied Energy, Elsevier, vol. 132(C), pages 557-567.
    3. Ghiaus, Christian, 2013. "Causality issue in the heat balance method for calculating the design heating and cooling load," Energy, Elsevier, vol. 50(C), pages 292-301.
    4. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    5. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    6. Desideri, Umberto & Leonardi, Daniela & Arcioni, Livia & Sdringola, Paolo, 2012. "European project Educa-RUE: An example of energy efficiency paths in educational buildings," Applied Energy, Elsevier, vol. 97(C), pages 384-395.
    7. Ghiaus, Christian, 2014. "Linear algebra solution to psychometric analysis of air-conditioning systems," Energy, Elsevier, vol. 74(C), pages 555-566.
    8. Talyor, Robert A. & Miner, Mark, 2014. "A metric for characterizing the effectiveness of thermal mass in building materials," Applied Energy, Elsevier, vol. 128(C), pages 156-163.
    9. 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.
    10. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.
    11. 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. Ghiaus, Christian & Ahmad, Naveed, 2020. "Thermal circuits assembling and state-space extraction for modelling heat transfer in buildings," Energy, Elsevier, vol. 195(C).

    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. 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.
    2. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
    3. 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.
    4. 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.
    5. Niemelä, Tuomo & Kosonen, Risto & Jokisalo, Juha, 2016. "Cost-optimal energy performance renovation measures of educational buildings in cold climate," Applied Energy, Elsevier, vol. 183(C), pages 1005-1020.
    6. 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.
    7. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    8. Ascione, Fabrizio & Bianco, Nicola & de’ Rossi, Filippo & Turni, Gianluca & Vanoli, Giuseppe Peter, 2013. "Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?," Applied Energy, Elsevier, vol. 104(C), pages 845-859.
    9. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    11. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
    12. Ohlsson, K.E. Anders & Olofsson, Thomas, 2021. "Benchmarking the practice of validation and uncertainty analysis of building energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    13. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    14. 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).
    15. 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.
    16. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
    17. 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.
    18. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    19. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    20. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(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:139:y:2015:i:c:p:230-244. 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.