IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p6749-d401554.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/6749/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/17/6749/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    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. 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.
    7. 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.
    8. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    9. 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.
    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. 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.
    12. von Grabe, Jörn, 2016. "Potential of artificial neural networks to predict thermal sensation votes," Applied Energy, Elsevier, vol. 161(C), pages 412-424.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    2. 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.
    3. Mark Bomberg & Anna Romanska-Zapala & Paulo Santos, 2023. "The 4th Industrial Revolution Brings a Change in the Design Paradigm for New and Retrofitted Buildings," Energies, MDPI, vol. 16(4), pages 1-22, February.
    4. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    5. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    6. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    7. 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.
    8. Małgorzata Fedorczak-Cisak & Alicja Kowalska-Koczwara & Filip Pachla & Elżbieta Radziszewska-Zielina & Bartłomiej Szewczyk & Grzegorz Śladowski & Tadeusz Tatara, 2020. "Fuzzy Model for Selecting a Form of Use Alternative for a Historic Building to be Subjected to Adaptive Reuse," Energies, MDPI, vol. 13(11), pages 1-24, June.
    9. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    10. Małgorzata Fedorczak-Cisak & Anna Kotowicz & Elżbieta Radziszewska-Zielina & Bartłomiej Sroka & Tadeusz Tatara & Krzysztof Barnaś, 2020. "Multi-Criteria Optimisation of an Experimental Complex of Single-Family Nearly Zero-Energy Buildings," Energies, MDPI, vol. 13(7), pages 1-30, March.
    11. Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
    12. 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.
    13. 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.
    14. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    15. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    16. Michał Piasecki & Elżbieta Radziszewska-Zielina & Piotr Czerski & Małgorzata Fedorczak-Cisak & Michał Zielina & Paweł Krzyściak & Patrycja Kwaśniewska-Sip & Wojciech Grześkowiak, 2020. "Implementation of the Indoor Environmental Quality (IEQ) Model for the Assessment of a Retrofitted Historical Masonry Building," Energies, MDPI, vol. 13(22), pages 1-27, November.
    17. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Villanthenkodath, Muhammed Ashiq & Mahalik, Mantu Kumar, 2021. "Does economic growth respond to electricity consumption asymmetrically in Bangladesh? The implication for environmental sustainability," Energy, Elsevier, vol. 233(C).
    19. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    20. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.

    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:gam:jsusta:v:12:y:2020:i:17:p:6749-:d:401554. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.