Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Huaiguang Jiang, Fei Ding, Yingchen Zhang

Research output: Contribution to conferencePaper


In the 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 load forecasting technique can provide accurate prediction of load power that will happen in 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 the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Original languageAmerican English
Number of pages8
StatePublished - 2017
Event2017 IEEE Power & Energy Society General Meeting (PES GM) - Chicago, Illinois
Duration: 16 Jul 201720 Jul 2017


Conference2017 IEEE Power & Energy Society General Meeting (PES GM)
CityChicago, Illinois

Bibliographical note

See NREL/CP-5D00-71649 for paper as published in IEEE proceedings

NREL Publication Number

  • NREL/CP-5D00-67762


  • distribution system
  • network reconfiguration
  • parallel parameters optimization
  • short-term load forecasting


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