A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid-to Long-Term Distribution Planning

Jintae Cho, Yeunggul Yoon, Yongju Son, Hongjoo Kim, Hosung Ryu, Gilsoo Jang

Research output: Contribution to journalArticlepeer-review

Abstract

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid-to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid-to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.

Original languageEnglish
Article number2987
JournalEnergies
Volume15
Issue number9
DOIs
Publication statusPublished - 2022 May 1

Keywords

  • distribution line
  • distribution system planning
  • hybrid forecasting model
  • peak load

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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