DocumentCode :
1269531
Title :
Cascaded artificial neural networks for short-term load forecasting
Author :
Alfuhaid, A.S. ; El-Sayed, M.A. ; Mahmoud, M.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Kuwait Univ., Safat, Kuwait
Volume :
12
Issue :
4
fYear :
1997
fDate :
11/1/1997 12:00:00 AM
Firstpage :
1524
Lastpage :
1529
Abstract :
An application of artificial neural networks (ANNs) to short-term load forecasting is presented in this paper. An algorithm using cascaded learning together with historical load and weather data is proposed to forecast half-hourly power system load for the next 24 hours. This cascaded neural network algorithm (CANNs) includes peak, minimum and daily energy prediction as additional input data for the final forecast stage. These additional input data are predicted using the first (ANNs) model. The networks are trained and tested on the electric power system of Kuwait. The absolute average forecasting error is reduced from 3.367% to 2.707% by applying CANNs as compared to the conventional ANNs. Simulation results indicate that the developed forecasting approach is effective and point to the potential of the methodology for economic applications
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; absolute average forecasting error; cascaded artificial neural networks; cascaded learning; computer simulation; daily energy prediction; half-hourly load forecasts; minimum energy prediction; peak energy prediction; power systems; short-term load forecasting; Artificial neural networks; Economic forecasting; Load forecasting; Power generation economics; Power system economics; Power system modeling; Power system simulation; Predictive models; System testing; Weather forecasting;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/59.627852
Filename :
627852
Link To Document :
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