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Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock

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  • Marcin Zygmunt

    (Department of Building Material Physics and Sustainable Design, Technical University of Lodz, 93-590 Lodz, Poland)

  • Dariusz Gawin

    (Department of Building Material Physics and Sustainable Design, Technical University of Lodz, 93-590 Lodz, Poland)

Abstract

The development of energy-efficient buildings and sustainable energy supply systems is an obligatory undertaking towards a more sustainable future. To protect the natural environment, the modernization of urban infrastructure is indisputably important, possible to achieve considering numerous buildings as a group, i.e., Building Energy Cluster (BEC). The urban planning process evaluates multiple complex criteria to select the most profitable scenario in terms of energy consumption, environmental protection, or financial profitability. Thus, Urban Building Energy Modelling (UBEM) is presently a popular approach applied for studies towards the development of sustainable cities. Today’s UBEM tools use various calculation methods and approaches, as well as include different assumptions and limitations. While there are several popular and valuable software for UBEM, there is still no such tool for analyses of the Polish residential stock. In this work an overview on the home-developed tool called TEAC, focusing on its’ mathematical model and use of Artificial Neural Networks (ANN). An exemplary application of the TEAC software is also presented.

Suggested Citation

  • Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8285-:d:698262
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