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XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation

Author

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  • Dong-Jin Bae

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Bo-Sung Kwon

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Kyung-Bin Song

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. In order to overcome the problems caused by the impact of BTM solar PV generation, an extreme gradient boosting (XGBoost) load forecasting algorithm is proposed. The capacity of the BTM solar PV generators is estimated based on an investigation of the deviation of load using a grid search. The influence of external factors was considered by using the fluctuation of the load used by lighting appliances and data filtering based on base temperature, as a result, the capacity of the BTM solar PV generators is accurately estimated. The distortion of electric load is eliminated by the reconstituted load method that adds the estimated BTM solar PV generation to the electric load, and the load forecasting is conducted using the XGBoost model. Case studies are performed to demonstrate the accuracy of prediction for the proposed method. The accuracy of the proposed algorithm was improved by 21% and 29% in 2019 and 2020, respectively, compared with the MAPE of the LSTM model that does not reflect the impact of BTM solar PV.

Suggested Citation

  • Dong-Jin Bae & Bo-Sung Kwon & Kyung-Bin Song, 2021. "XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation," Energies, MDPI, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:128-:d:710863
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    References listed on IDEAS

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    1. Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    2. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
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    Cited by:

    1. Maleki, Neda & Lundström, Oxana & Musaddiq, Arslan & Jeansson, John & Olsson, Tobias & Ahlgren, Fredrik, 2024. "Future energy insights: Time-series and deep learning models for city load forecasting," Applied Energy, Elsevier, vol. 374(C).
    2. Periklis Gogas & Theophilos Papadimitriou, 2023. "Machine Learning in Renewable Energy," Energies, MDPI, vol. 16(5), pages 1-3, February.
    3. Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
    4. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    5. Chibuike Chiedozie Ibebuchi, 2025. "Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors," Forecasting, MDPI, vol. 7(2), pages 1-16, April.

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