IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v27y2002i9p889-904.html
   My bibliography  Save this article

Prediction of the cop of existing rooftop units using artificial neural networks and minimum number of sensors

Author

Listed:
  • Zmeureanu, R.

Abstract

This paper proposes a new approach for evaluating the Coefficient of Performance (COP) of existing rooftop units, using the General Regression Neural Networks. This approach reduces the installation cost of monitoring equipment since only a minimum number of sensors is needed, and it also reduces the costs for re-calibration or replacement of sensors during the operation. The new approach was developed and tested using measurements taken on two existing rooftop units in Montreal, Canada.

Suggested Citation

  • Zmeureanu, R., 2002. "Prediction of the cop of existing rooftop units using artificial neural networks and minimum number of sensors," Energy, Elsevier, vol. 27(9), pages 889-904.
    • Handle: RePEc:eee:energy:v:27:y:2002:i:9:p:889-904
    DOI: 10.1016/S0360-5442(02)00027-0
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544202000270
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0360-5442(02)00027-0?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.

    Citations

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


    Cited by:

    1. 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.
    2. Pickering, B. & Choudhary, R., 2019. "District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles," Applied Energy, Elsevier, vol. 236(C), pages 1138-1157.
    3. Tsang, S.W. & Jim, C.Y., 2011. "Theoretical evaluation of thermal and energy performance of tropical green roofs," Energy, Elsevier, vol. 36(5), pages 3590-3598.

    More about this item

    Statistics

    Access and download statistics

    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:energy:v:27:y:2002:i:9:p:889-904. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.journals.elsevier.com/energy .

    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.