Short-Term Electric Load Forecasting Based on a Neural Fuzzy Network

S. H. Ling, Hung Fat Frank Leung, H. K. Lam, Peter K S Tam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

79 Citations (Scopus)

Abstract

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.
Original languageEnglish
Pages (from-to)1305-1316
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume50
Issue number6
DOIs
Publication statusPublished - 1 Dec 2003

Keywords

  • Genetic algorithm (GA)
  • Home networking
  • Load forecasting
  • Neural fuzzy network (NFN)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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