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A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea

Author

Listed:
  • Sol Kim

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

  • Sungwon Jung

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

  • Seung-Man Baek

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

Abstract

Residential energy consumption accounts for the majority of building energy consumption. Physical factors and technological developments to address this problem have been researched continuously. However, physical improvements have limitations, and there is a paradigm shift towards energy research based on occupant behavior. Furthermore, the rapid increase in the number of single-person households around the world is decreasing residential energy efficiency, which is an urgent problem that needs to be solved. This study prepared a large dataset for analysis based on the Korean Time Use Survey (KTUS), which provides behavioral data for actual occupants of single-person households, and energy usage pattern (EUP) types that were derived through K-modes clustering. The characteristics and energy consumption of each type of household were analyzed, and their relationships were examined. Finally, an EUP-type predictive model, with a prediction rate of 95.0%, was implemented by training a support vector machine, and an energy consumption information model based on a Gaussian process regression was provided. The results of this study provide useful basic data for future research on energy consumption based on the behaviors of occupants, and the method proposed in this study will also be applicable to other regions.

Suggested Citation

  • Sol Kim & Sungwon Jung & Seung-Man Baek, 2019. "A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:1:p:245-:d:195292
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    References listed on IDEAS

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    2. Vincent Le & Joshua Ramirez & Miltiadis Alamaniotis, 2021. "Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy," Energies, MDPI, vol. 14(20), pages 1-13, October.
    3. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    4. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    5. Yuanping Wang & Lang Hu & Lingchun Hou & Lin Wang & Juntao Chen & Yu He & Xinyue Su, 2024. "A SHAP machine learning-based study of factors influencing urban residents' electricity consumption - evidence from chinese provincial data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(12), pages 30445-30476, December.

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