Title :
Fuzzy neural network and fuzzy expert system for load forecasting
Author :
Dash, P.K. ; Liew, A.C. ; Rahman, S.
Author_Institution :
Dept. of Electr. Eng., Regional Eng. Coll., Rourkela, India
fDate :
1/1/1996 12:00:00 AM
Abstract :
A hybrid neural network fuzzy expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of the load and other weather variables are the inputs to the neural network, and the output comprises the membership values of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. Extensive studies have been performed for all seasons, and a few examples are presented in the paper, average, peak and hourly load forecasts
Keywords :
expert systems; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); load forecasting; power system analysis computing; adaptive fuzzy correction scheme; fuzzy inference mechanism; fuzzy membership values; fuzzy rule base; hourly load forecasts; load forecasting; neural network fuzzy expert system; peak load forecasts; short-term electric load; weather variables;
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
DOI :
10.1049/ip-gtd:19960314