A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

Huaiguang Jiang, Yingchen Zhang, Eduard Muljadi, Jun Zhang, David Gao

Research output: Contribution to journalArticlepeer-review

172 Scopus Citations

Abstract

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.
Original languageAmerican English
Pages (from-to)3341-3350
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume9
Issue number4
DOIs
StatePublished - 2018

NREL Publication Number

  • NREL/JA-5D00-68193

Keywords

  • distribution system
  • grid traverse algorithm
  • particle swarm optimization
  • short-term load forecast
  • support vector regression

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