Title :
Short-term electric load forecasting using neural network models
Author :
Al-Rashid, Yasser ; Paarmann, Larry D.
Author_Institution :
Dept. of Electr. Eng., Wichita State Univ., KS, USA
Abstract :
Short-term power load forecasting is used to provide utility company management with future information about electric load demand in order to assist them in running more economical and reliable day-to-day operations. An Artificial Neural Network (ANN) approach is used in this paper to construct a 24 hour ahead power load forecasting model for the winter and summer seasons. The proposed ANN models were tested by forecasting the electric load for the Wichita, Kansas, area throughout 1992. Then the forecasted results were compared to the actual load and the performance was evaluated and compared with that of a Time Series, ARMA, model
Keywords :
load forecasting; neural nets; power system analysis computing; 24 hour; artificial neural network model; short-term electric load forecasting; summer season; utility company management; winter season; Artificial neural networks; Economic forecasting; Energy management; Information management; Load forecasting; Load modeling; Neural networks; Power generation economics; Predictive models; Testing;
Conference_Titel :
Circuits and Systems, 1996., IEEE 39th Midwest symposium on
Conference_Location :
Ames, IA
Print_ISBN :
0-7803-3636-4
DOI :
10.1109/MWSCAS.1996.593237
