Short-term daily load forecasting in an intelligent home with GA-based neural network

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

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

5 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 short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages997-1001
Number of pages5
Publication statusPublished - 1 Jan 2002
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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