Title :
Neural network with fuzzy set-based classification for short-term load forecasting
Author :
Daneshdoost, M. ; Lotfalian, M. ; Bumroonggit, G. ; Ngoy, J.P.
Author_Institution :
Dept. of Electr. Eng., Southern Illinois Univ., Carbondale, IL, USA
fDate :
11/1/1998 12:00:00 AM
Abstract :
Electric power utilities require forecast of system demand or electrical load for one to seven days ahead. This paper studies a short-term electric load forecasting technique using a multi-layered feedforward artificial neural network (ANN) and a fuzzy set-based classification algorithm. The hourly data is subdivided into various class of weather conditions using the fuzzy set representation of weather variables and then the ANN´s are trained and used to perform the load forecasting up to 120 hours ahead with a remarkable accuracy
Keywords :
feedforward neural nets; fuzzy set theory; learning (artificial intelligence); load forecasting; multilayer perceptrons; power system analysis computing; ANN training; electric power utilities; fuzzy set representation; fuzzy set-based classification; multi-layered feedforward artificial neural network; short-term load forecasting; system demand forecasting; weather conditions; weather variables; Artificial neural networks; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Humidity; Load forecasting; Neural networks; Predictive models; Temperature; Weather forecasting;
Journal_Title :
Power Systems, IEEE Transactions on
