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

7 Citations (Scopus)


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
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


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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this