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