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Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses

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  • Sungjin Lee

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
    Department of Architecture and Architectural Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Soo Cho

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Seo-Hoon Kim

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Jonghun Kim

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Suyong Chae

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Hakgeun Jeong

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Taeyeon Kim

    (Department of Architecture and Architectural Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy ( R 2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy ( Cv ( RMSE )) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

Suggested Citation

  • Sungjin Lee & Soo Cho & Seo-Hoon Kim & Jonghun Kim & Suyong Chae & Hakgeun Jeong & Taeyeon Kim, 2020. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses," Energies, MDPI, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:122-:d:469551
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    References listed on IDEAS

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    Cited by:

    1. Younhee Choi & Doosam Song & Sungmin Yoon & Junemo Koo, 2021. "Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads," Energies, MDPI, vol. 14(2), pages 1-23, January.
    2. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.

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