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Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

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  • Mansu Kim

    (Department of Architecture, Sejong University, Seoul 05006, Korea)

  • Sungwon Jung

    (Department of Architecture, Sejong University, Seoul 05006, Korea)

  • Joo-won Kang

    (Department of Architecture, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.

Suggested Citation

  • Mansu Kim & Sungwon Jung & Joo-won Kang, 2019. "Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea," Sustainability, MDPI, vol. 12(1), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:109-:d:300805
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    References listed on IDEAS

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    1. Pan, Dongmei & Chan, Mingyin & Deng, Shiming & Lin, Zhongping, 2012. "The effects of external wall insulation thickness on annual cooling and heating energy uses under different climates," Applied Energy, Elsevier, vol. 97(C), pages 313-318.
    2. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    3. Dongjun Suh & Seongju Chang, 2014. "A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings," Energies, MDPI, vol. 7(11), pages 1-33, October.
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    2. Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.

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