Daily load forecasting with a fuzzy-input-neural network in an intelligent home

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

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

4 Citations (Scopus)

Abstract

Daily 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 fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages449-452
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

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

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