KEPCO Journal on Electric Power and Energy

Korea Electric Power Corporation (한국전력공사)
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Prediction of Electric Power on Distribution Line Using Machine Learning and Actual Data Considering Distribution Plan

배전계획을 고려한 실데이터 및 기계학습 기반의 배전선로 부하예측 기법에 대한 연구

  • Kim, Junhyuk (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Lee, Byung-Sung (KEPCO Research Institute, Korea Electric Power Corporation)
  • Received : 2020.07.28
  • Accepted : 2020.10.29
  • Published : 2021.06.30
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Abstract

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

Keywords

Acknowledgement

본 연구는 한국전력공사의 주력연구개발과제 연구비에 의해 지원되었음

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