Electric load forecasting using an artificial neural network

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 dat...

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Bibliographic Details
Published in:IEEE transactions on power systems 1991-05, Vol.6 (2), p.442-449
Main Authors: Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.
Format: Article
Language:English
Subjects:
Artificial neural networks
Economic forecasting
Information security
Load forecasting
Power engineering computing
Power generation economics
Power systems
Temperature
Training data
Weather forecasting
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Summary: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.< >
ISSN:0885-8950
1558-0679
DOI:10.1109/59.76685