An advanced evolutionary algorithm for load forecasting with the Kalman filter

Zeke S.H. Chan, H. W. Ngan, Y. F. Fung, A. B. Rad

Research output: Journal article publicationConference articleAcademic researchpeer-review

7 Citations (Scopus)


In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the "Selection Follower". Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.

Original languageEnglish
Article number59848
Pages (from-to)134-138
Number of pages5
JournalIEE Conference Publication
Issue number478 I
Publication statusPublished - Nov 2000
Event5th International Conference on Advances in Power System Control, Operation and Management - Tsimshatsui, Kowloon, Hong Kong
Duration: 30 Oct 20001 Nov 2000


  • Adaptive mutation
  • Evolutionary algorithm
  • Genetic algorithm
  • Kalman filter
  • Load forecasting

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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