IDEAS home Printed from https://ideas.repec.org/a/taf/nmcmxx/v8y2002i3p333-344.html
   My bibliography  Save this article

Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System

Author

Listed:
  • M.M. Gouda
  • S. Danaher
  • C.P. Underwood

Abstract

Artificial neural networks (ANNs) have been used for modelling the thermal dynamics of a building’s space, its water heating system and the influence of solar radiation. A multi-layer feed-forward neural network, using a Levenberg-Marquardt backpropagation-training algorithm, has been applied to predict the future internal temperature. Real weather data for a number of winter months, together with a validated building model (based on the building constructions data), were used to train the network in order to generate a mapping between the easily measurable inputs (outdoor temperature, solar irradiance, heating valve position and the building indoor temperature) and the desired output, i.e., the predicted indoor temperature. The objective of this work was to investigate the potential of using an ANN with singular value decomposition method (SVD) to predict the indoor temperature to shut down the heating system controller early for saving the energy consumption for heating inside the building.

Suggested Citation

  • M.M. Gouda & S. Danaher & C.P. Underwood, 2002. "Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(3), pages 333-344, September.
    • Handle: RePEc:taf:nmcmxx:v:8:y:2002:i:3:p:333-344
    DOI: 10.1076/mcmd.8.3.333.14097
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1076/mcmd.8.3.333.14097
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1076/mcmd.8.3.333.14097?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. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    2. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    3. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.

    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:taf:nmcmxx:v:8:y:2002:i:3:p:333-344. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/NMCM20 .

    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.