IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1201-d217801.html
   My bibliography  Save this article

Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

Author

Listed:
  • Moon Keun Kim

    (Department of Architecture, Xi’an Jiatong-Liverpool University, Suzhou 215123, China)

  • Jaehoon Cha

    (Department of Electrical and Electronic Engineering, Xi’an Jiatong-Liverpool University, Suzhou 215123, China)

  • Eunmi Lee

    (Social Science Research Institute, Yonsei University, Seoul 03722, Korea)

  • Van Huy Pham

    (Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Sanghyuk Lee

    (Department of Electrical and Electronic Engineering, Xi’an Jiatong-Liverpool University, Suzhou 215123, China
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
    Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Nipon Theera-Umpon

    (Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Electrical Engineering, Faculty of Engineering Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.

Suggested Citation

  • Moon Keun Kim & Jaehoon Cha & Eunmi Lee & Van Huy Pham & Sanghyuk Lee & Nipon Theera-Umpon, 2019. "Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building," Energies, MDPI, vol. 12(7), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1201-:d:217801
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1201/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1201/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cesare Biserni & Paolo Valdiserri & Dario D’Orazio & Massimo Garai, 2018. "Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills," Energies, MDPI, vol. 11(8), pages 1-15, August.
    2. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "Economic Analysis of a Photovoltaic System: A Resource for Residential Households," Energies, MDPI, vol. 10(6), pages 1-15, June.
    3. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    4. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    5. Giuliano Dall'O' & Maria Franca Norese & Annalisa Galante & Chiara Novello, 2013. "A Multi-Criteria Methodology to Support Public Administration Decision Making Concerning Sustainable Energy Action Plans," Energies, MDPI, vol. 6(8), pages 1-23, August.
    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. Zhiqi Yan & Shisheng Zhong & Lin Lin & Zhiquan Cui, 2021. "Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks," Mathematics, MDPI, vol. 9(17), pages 1-17, September.
    2. Abdelhamid Zaidi, 2024. "Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 172-183, January.
    3. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    4. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.

    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. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    2. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    3. Anna Życzyńska & Dariusz Majerek & Zbigniew Suchorab & Agnieszka Żelazna & Václav Kočí & Robert Černý, 2021. "Improving the Energy Performance of Public Buildings Equipped with Individual Gas Boilers Due to Thermal Retrofitting," Energies, MDPI, vol. 14(6), pages 1-19, March.
    4. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    5. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    6. Di Leo, Senatro & Salvia, Monica, 2017. "Local strategies and action plans towards resource efficiency in South East Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 286-305.
    7. Soheil Kavian & Mohsen Saffari Pour & Ali Hakkaki-Fard, 2019. "Optimized Design of the District Heating System by Considering the Techno-Economic Aspects and Future Weather Projection," Energies, MDPI, vol. 12(9), pages 1-30, May.
    8. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    9. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    10. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
    11. Lobaccaro, G. & Croce, S. & Lindkvist, C. & Munari Probst, M.C. & Scognamiglio, A. & Dahlberg, J. & Lundgren, M. & Wall, M., 2019. "A cross-country perspective on solar energy in urban planning: Lessons learned from international case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 209-237.
    12. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    13. D’Adamo, Idiano & Falcone, Pasquale Marcello & Gastaldi, Massimo & Morone, Piergiuseppe, 2020. "The economic viability of photovoltaic systems in public buildings: Evidence from Italy," Energy, Elsevier, vol. 207(C).
    14. George M. Stavrakakis & Dimitris Bakirtzis & Korina-Konstantina Drakaki & Sofia Yfanti & Dimitris Al. Katsaprakakis & Konstantinos Braimakis & Panagiotis Langouranis & Konstantinos Terzis & Panagiotis, 2024. "Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece," Energies, MDPI, vol. 17(3), pages 1-30, January.
    15. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    16. Xiangwu Yan & Jiajia Li & Ling Wang & Shuaishuai Zhao & Tie Li & Zhipeng Lv & Ming Wu, 2018. "Adaptive-MPPT-Based Control of Improved Photovoltaic Virtual Synchronous Generators," Energies, MDPI, vol. 11(7), pages 1-18, July.
    17. Henrik Zsiborács & Gábor Pintér & Attila Bai & József Popp & Zoltán Gabnai & Béla Pályi & István Farkas & Nóra Hegedűsné Baranyai & Christian Gützer & Heidelinde Trimmel & Sandro Oswald & Philipp Weih, 2018. "Comparison of Thermal Models for Ground-Mounted South-Facing Photovoltaic Technologies: A Practical Case Study," Energies, MDPI, vol. 11(5), pages 1-18, May.
    18. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    19. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    20. Anna Życzyńska & Zbigniew Suchorab & Jan Kočí & Robert Černý, 2020. "Energy Effects of Retrofitting the Educational Facilities Located in South-Eastern Poland," Energies, MDPI, vol. 13(10), pages 1-16, May.

    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:jeners:v:12:y:2019:i:7:p:1201-:d:217801. 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.