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Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Author

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  • Sooyoun Cho

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

  • Jeehang Lee

    (Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Jumi Baek

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

  • Gi-Seok Kim

    (Center for Sustainable Buildings, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Seung-Bok Leigh

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

Abstract

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

Suggested Citation

  • Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4046-:d:279767
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    References listed on IDEAS

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    Cited by:

    1. Yuting Qi & Queena Qian & Frits Meijer & Henk Visscher, 2020. "Causes of Quality Failures in Building Energy Renovation Projects of Northern China: A Review and Empirical Study," Energies, MDPI, vol. 13(10), pages 1-19, May.
    2. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    3. Jihoon Jang & Joosang Lee & Eunjo Son & Kyungyong Park & Gahee Kim & Jee Hang Lee & Seung-Bok Leigh, 2019. "Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection," Energies, MDPI, vol. 12(21), pages 1-20, November.
    4. Constantinos A. Balaras, 2022. "Building Energy Audits—Diagnosis and Retrofitting towards Decarbonization and Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-4, March.

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