Title :
Electric load forecasting using an artificial neural network
Author :
Park, D.C. ; El-Sharkawi, M.A. ; Marks, R.J., II ; Atlas, L.E. ; Damborg, M.J.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
fDate :
5/1/1991 12:00:00 AM
Abstract :
An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data
Keywords :
load forecasting; neural nets; power engineering computing; artificial neural network; data interpolation; load data; temperature data; Artificial neural networks; Economic forecasting; Information security; Load forecasting; Power engineering computing; Power generation economics; Power systems; Temperature; Training data; Weather forecasting;
Journal_Title :
Power Systems, IEEE Transactions on
