A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages1456-1459
Number of pages4
Publication statusPublished - 1 Dec 2001
Event10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia
Duration: 2 Dec 20015 Dec 2001

Conference

Conference10th IEEE International Conference on Fuzzy Systems
Country/TerritoryAustralia
CityMelbourne
Period2/12/015/12/01

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety

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