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

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    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.

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