Fuzzy logic for short term load forecasting

D. K. Ranaweera, N. F. Hubele, G. G. Karady

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods.

Original languageEnglish (US)
Pages (from-to)215-222
Number of pages8
JournalInternational Journal of Electrical Power and Energy Systems
Issue number4
StatePublished - May 1996


  • Back-propagation network
  • Fuzzy logic
  • Learning algorithm
  • Short term load forecasting

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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