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Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer

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  • Marek Dudzik

    (Department of Traction and Traffic Control, Faculty of Electrical and Computer Engineering, Cracow University of Technology, 31-155 Cracow, Poland)

Abstract

Modelling of comfort with the use of neural networks in modern times has become extremely popular. In recent years, scientists have been using these methods because of their satisfactory accuracy. The article proposes a method of modelling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of a given quality criterion. The article also presents the methodology for modelling a PMV index, on the basis of which it can be demonstrated whether the network will work properly not only on paper but in reality as well. The objective of this work is to develop a performance model allowing the effective improvement of all electrical and mechanical devices affecting the energy efficiency and indoor environment in smart buildings. To achieve this, several attributes of indoor environment are included, namely: air leakage as a connection to the outdoor environment, but also as uncontrolled component of energy, ventilation as delivery and distribution of fresh air in the building space, individual ventilation on demand indoor air quality (IAQ) in the dwelling or as a personal IAQ control, source control of pollutants in the building, thermal comfort, temperature, air movement and humidity control (humidity modifiers, i.e., buffers different from the air conditioning radiation from cold and hot surfaces bringing forward a question about the strategy of the process control. One may either develop a series of control models to be synthesized later or one can use one over-arching characteristic and use its components for operating the control system. The paper addresses the second strategy and uses the concept of PMV for a criterion of broadly defined thermal comfort (including ventilation and air quality).

Suggested Citation

  • Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6749-:d:401554
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    References listed on IDEAS

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    Cited by:

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    2. Zofia Wróbel & Adam St. Jagiełło, 2021. "The Risk of Lightning Losses in a Structure Equipped with RTC Devices According to the Standard EN 62305-2.2008," Energies, MDPI, vol. 14(6), pages 1-18, March.
    3. Dmitry Kaplun & Alexander Krasichkov & Petr Chetyrbok & Nikolay Oleinikov & Anupam Garg & Husanbir Singh Pannu, 2021. "Cancer Cell Profiling Using Image Moments and Neural Networks with Model Agnostic Explainability: A Case Study of Breast Cancer Histopathological (BreakHis) Database," Mathematics, MDPI, vol. 9(20), pages 1-20, October.
    4. Przemysław Markiewicz-Zahorski & Joanna Rucińska & Małgorzata Fedorczak-Cisak & Michał Zielina, 2021. "Building Energy Performance Analysis after Changing Its Form of Use from an Office to a Residential Building," Energies, MDPI, vol. 14(3), pages 1-24, January.

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