Title :
A study on neural networks for short-term load forecasting
Author :
Lee, K.Y. ; Cha, Y.T. ; Ku, C.C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
A study is made on the application of the artificial neural network (ANN) method 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 include Saturday, Sunday, and Monday loads. Three different ANN models are proposed, including two feedforward neural networks and one recurrent neural network. Inputs to the ANN are past loads and the output is the predicted load for a given day. The standard deviation and percent error of each model are compared
Keywords :
feedforward neural nets; load forecasting; power engineering computing; feedforward neural networks; neural networks; power system; short-term load forecasting; weekday pattern; weekend-day patterns; Application software; Artificial neural networks; Backpropagation algorithms; Load forecasting; Load modeling; Neural networks; Power system modeling; Predictive models; Recurrent neural networks; Weather forecasting;
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
DOI :
10.1109/ANN.1991.213492
