A novel GA-based neural network for short-term load forecasting

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

6 Citations (Scopus)

Abstract

This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages2761-2766
Number of pages6
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)
CountryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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