Computational intelligence techniques for home electric load forecasting and balancing

S.H. Ling, Hung Fat Frank Leung, L.K. Wong, H.K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on achieving the load balancing.
Original languageEnglish
Pages (from-to)371-391
Number of pages21
JournalInternational Journal of Computational Intelligence and Applications
Issue number3
Publication statusPublished - 2005


  • Genetic algorithms
  • Load balancing
  • Neural networks
  • Short-term load forecasting

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Theoretical Computer Science


Dive into the research topics of 'Computational intelligence techniques for home electric load forecasting and balancing'. Together they form a unique fingerprint.

Cite this