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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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    1. Marek Dudzik & Anna Romanska-Zapala & Mark Bomberg, 2020. "A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Aiman Albatayneh & Dariusz Alterman & Adrian Page & Behdad Moghtaderi, 2018. "The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    3. Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    4. Małgorzata Fedorczak-Cisak & Alicja Kowalska-Koczwara & Krzysztof Nering & Filip Pachla & Elżbieta Radziszewska-Zielina & Grzegorz Śladowski & Tadeusz Tatara & Bartłomiej Ziarko, 2019. "Evaluation of the Criteria for Selecting Proposed Variants of Utility Functions in the Adaptation of Historic Regional Architecture," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
    5. Cinzia Buratti & Elisa Lascaro & Domenico Palladino & Marco Vergoni, 2014. "Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions," Sustainability, MDPI, vol. 6(8), pages 1-15, August.
    6. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    7. Mark Bomberg & Anna Romanska-Zapala & David Yarbrough, 2020. "Journey of American Building Physics: Steps Leading to the Current Scientific Revolution," Energies, MDPI, vol. 13(5), pages 1-12, February.
    8. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    9. von Grabe, Jörn, 2016. "Potential of artificial neural networks to predict thermal sensation votes," Applied Energy, Elsevier, vol. 161(C), pages 412-424.
    10. Guofeng Ma & Ying Liu & Shanshan Shang, 2019. "A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    11. Anupama Sharma & Richa Tiwari, 2007. "Evaluation of data for developing an adaptive model of thermal comfort and preference," Environment Systems and Decisions, Springer, vol. 27(1), pages 73-81, March.
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    Cited by:

    1. Prabhakar Krishnan & A V Prabu & Sumathi Loganathan & Sidheswar Routray & Uttam Ghosh & Mohammed AL-Numay, 2023. "Analyzing and Managing Various Energy-Related Environmental Factors for Providing Personalized IoT Services for Smart Buildings in Smart Environment," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    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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