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Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

Author

Listed:
  • Jihoon Moon

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Junhong Kim

    (School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Pilsung Kang

    (School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Eenjun Hwang

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

Abstract

An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building.

Suggested Citation

  • Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:886-:d:321583
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    References listed on IDEAS

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    Cited by:

    1. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
    2. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
    3. Seung-Min Jung & Sungwoo Park & Seung-Won Jung & Eenjun Hwang, 2020. "Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-20, August.

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