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<p>With the recent advancement of artificial intelligence (AI) technology and the development of hardware that enables the use of AI technology, attempts are being made to incorporate AI into various fields. Various studies using AI have also been attempted in the power system field. In this paper, it is summarized and reviewed that the actual performance of distribution system operation experience of machine learning-based load predictions of distribution lines, in terms of asset management in Korea Electric Power Corporation (KEPCO), a Korean utility company. It was confirmed that the development method and system showed an average accuracy of 89.56% for 10,832 distribution lines in Korea, and based on this, it was assessed that the actual distribution plan investment cost could potentially be reduced.</p>

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A practical load prediction framework for distribution lines: case study at Korea electric power corporation

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Jun-Hyeok Kim

摘要

With the recent advancement of artificial intelligence (AI) technology and the development of hardware that enables the use of AI technology, attempts are being made to incorporate AI into various fields. Various studies using AI have also been attempted in the power system field. In this paper, it is summarized and reviewed that the actual performance of distribution system operation experience of machine learning-based load predictions of distribution lines, in terms of asset management in Korea Electric Power Corporation (KEPCO), a Korean utility company. It was confirmed that the development method and system showed an average accuracy of 89.56% for 10,832 distribution lines in Korea, and based on this, it was assessed that the actual distribution plan investment cost could potentially be reduced.