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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
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    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.

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