Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

Huaiguang Jiang, Fei Ding, Yingchen Zhang

Research output: Contribution to conferencePaperpeer-review

10 Scopus Citations


In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Original languageAmerican English
Number of pages5
StatePublished - 29 Jan 2018
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: 16 Jul 201720 Jul 2017


Conference2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-67762 for preprint

NREL Publication Number

  • NREL/CP-5D00-71649


  • Distribution system
  • Network reconfiguration
  • Parallel parameters optimization
  • Short-term load forecasting


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