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Regression-Based Methods for Daily Peak Load Forecasting in South Korea

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  • Geun-Cheol Lee

    (College of Business Administration, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

Abstract

This study examines the daily peak load forecasting problem in South Korea. This problem has become increasingly important due to the continually changing energy environment. As such, it has been studied by many researchers over the decades. South Korea is geographically located such that it experiences four distinct seasons. Seasonal changes are among the main factors affecting electricity demand. In addition, much of the electricity consumption in a strong manufacturing country like South Korea is driven by industry rather than by residential customers. In order to forecast daily peak loads of South Korea, in this study we proposed multiple linear regression-based methods where several season-specific regression models (i.e., summer, winter, and all-season models) were included. The most appropriate model among the three models was selected considering the characteristics of the electricity demand, and was then applied to daily forecasting. The performance of the proposed methods were evaluated through computational experiments. Forecasts obtained by the proposed methods were compared with those obtained by existing forecasting methods, including a machine learning method. The results showed that the proposed methods had mean absolute percentage errors around 1.95% and outperformed all benchmarks.

Suggested Citation

  • Geun-Cheol Lee, 2022. "Regression-Based Methods for Daily Peak Load Forecasting in South Korea," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3984-:d:781373
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    References listed on IDEAS

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    Cited by:

    1. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    2. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    3. Moshe Kelner & Zinoviy Landsman & Udi E. Makov, 2022. "Probabilistic Peak Demand Estimation Using Members of the Clayton Generalized Gamma Copula Family," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    5. Kaiyan Wang & Haodong Du & Jiao Wang & Rong Jia & Zhenyu Zong, 2023. "An Ensemble Deep Learning Model for Provincial Load Forecasting Based on Reduced Dimensional Clustering and Decomposition Strategies," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    6. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.

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