Title :
Short-term load forecasting using an artificial neural network
Author :
Lee, K.Y. ; Cha, Y.T. ; Park, J.H.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
fDate :
2/1/1992 12:00:00 AM
Abstract :
An artificial neural network (ANN) method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of an ANN for short-term load forecasting were tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers was tested with various combinations of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives a good load forecast
Keywords :
load forecasting; neural nets; power engineering computing; artificial neural network; forecasting error; hidden layers; nonlinear load model; power system; short-term load forecasting; weekday pattern; weekend-day pattern; Artificial neural networks; Economic forecasting; Load forecasting; Load modeling; Neural networks; Power system modeling; Power system planning; Predictive models; Testing; Weather forecasting;
Journal_Title :
Power Systems, IEEE Transactions on
