IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v4y2009i2p112-119.html
   My bibliography  Save this article

An artificial neural network for predicting domestic hot water characteristics

Author

Listed:
  • Christian Barteczko-Hibbert
  • Mark Gillott
  • Graham Kendall

Abstract

Domestic hot water (DHW) in the UK accounts for ∼7.5% of all energy use. For manufacturers of heating and hot water appliances to be in a position to respond to patterns of demand a full understanding of the effect of user-defined DHW profiles, different DHW systems and heating technologies are essential. This paper presents the prediction of the temperature characteristics of drawn DHW using artificial neural networks (NNs). We demonstrate whether, based on one NN model, different hot water system temperature loads can be accurately predicted. Two NN models were constructed and examined on a total of three systems. Both models trained on their associated systems produced errors of <11%; however, both NN models, when presented with unseen systems, produced large single errors. NN model 2 gave the lowest error when compared with NN model 1. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • Christian Barteczko-Hibbert & Mark Gillott & Graham Kendall, 2009. "An artificial neural network for predicting domestic hot water characteristics," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 4(2), pages 112-119, April.
    • Handle: RePEc:oup:ijlctc:v:4:y:2009:i:2:p:112-119
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctp010
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Bo Lin & Shuhui Li & Yang Xiao, 2017. "Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System," Energies, MDPI, vol. 10(11), pages 1-17, October.
    2. Wojciech Rzeźnik & Ilona Rzeźnik & Paweł Hara, 2022. "Comparison of Real and Forecasted Domestic Hot Water Consumption and Demand for Heat Power in Multifamily Buildings, in Poland," Energies, MDPI, vol. 15(19), pages 1-17, September.
    3. Meireles, I. & Sousa, V. & Bleys, B. & Poncelet, B., 2022. "Domestic hot water consumption pattern: Relation with total water consumption and air temperature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    4. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.

    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:oup:ijlctc:v:4:y:2009:i:2:p:112-119. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    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.