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

fYear

1991

fDate

23-26 Jul 1991

Firstpage

26

Lastpage

30

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ANN.1991.213492

Filename

213492