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.
Bibliographical noteFunding Information:
Funding: This research was supported in part by the KEPCO Research Institute under the project entitled by “A Research of Advanced Distribution Planning System for Mid-long term (R20DA16)” and in part by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (No. 2020R1A4A1019405).
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- 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