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A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

Author

Listed:
  • Jintae Cho

    (School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea)

  • Yeunggul Yoon

    (School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea)

  • Yongju Son

    (School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea)

  • Hongjoo Kim

    (KEPCO Research Institute (KEPRI), 105 Munji-ro, Yuseong-gu, Daejeon 34056, Korea)

  • Hosung Ryu

    (KEPCO Research Institute (KEPRI), 105 Munji-ro, Yuseong-gu, Daejeon 34056, Korea)

  • Gilsoo Jang

    (School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea)

Abstract

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.

Suggested Citation

  • Jintae Cho & Yeunggul Yoon & Yongju Son & Hongjoo Kim & Hosung Ryu & Gilsoo Jang, 2022. "A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning," Energies, MDPI, vol. 15(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:2987-:d:797167
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    References listed on IDEAS

    as
    1. Juyong Kim & Hongjoo Kim & Jintae Cho & Youngpyo Cho & Yoonsung Cho & Sukcheol Kim, 2020. "Demonstration Study of Voltage Control of DC Grid Using Energy Management System Based DC Applications," Energies, MDPI, vol. 13(17), pages 1-23, September.
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    Cited by:

    1. Aoqi Xu & Man-Wen Tian & Behnam Firouzi & Khalid A. Alattas & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting," Sustainability, MDPI, vol. 14(16), pages 1-12, August.

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