Title :
Neural network based short term load forecasting
Author :
Lu, C.N. ; Wu, H.-T. ; Vemuri, S.
Author_Institution :
Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kahosiung, Taiwan
fDate :
2/1/1993 12:00:00 AM
Abstract :
The artificial neural network (ANN) technique for short-term load forecasting (STLF) has been proposed previously. In order to evaluate ANNs as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. The authors make an attempt to address these issues. The results of a study to investigate whether the ANN model is system dependent, and/or case dependent, are presented. Data from two utilities are used in modeling and forecasting. In addition, the effectiveness of a next 24 h ANN model in predicting 24 h load profile at one time was compared with the traditional next 1 h ANN model
Keywords :
load forecasting; neural nets; power engineering computing; 24 h; artificial neural network; load profile; short term load forecasting; Artificial neural networks; Economic forecasting; Load forecasting; Neural networks; Power generation economics; Power system economics; Power system modeling; Power system reliability; Predictive models; Testing;
Journal_Title :
Power Systems, IEEE Transactions on