Short-term load forecasting based on adaptive Neuro-Fuzzy inference system

Thai Nguyen, Yuan Liao

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Accurate load forecasting helps stabilize the system by triggering the appropriate actions if needed such as planning for emergency dispatch and load switching for short-term solution and building or upgrading facilities for long-term solution. The Short Term Load Forecasting (STLF) provides information for utilities' system planners so that they can come up with a short-term solution to protect the transmission and distribution systems and to better serve the customers. This article provides a way of accurately predicting one-hour-ahead load of a utility company located in the North America region (hereafter this utility will be referred to as NAUC) based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS are the next-hour temperature, next-hour dew point, day of the week, hour of the day, and the current-hour load. The output is the next-hour load of the entire system. The ANFIS based method can accurately predict the next-hour load to an accuracy of ± 2.5%.

Original languageEnglish
Pages (from-to)2267-2271
Number of pages5
JournalJournal of Computers
Issue number11
StatePublished - 2011


  • Adaptive neuro-fuzzy inference system
  • Short-term load forecasting

ASJC Scopus subject areas

  • General Computer Science


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