DocumentCode
917803
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
Volume
8
Issue
1
fYear
1993
fDate
2/1/1993 12:00:00 AM
Firstpage
336
Lastpage
342
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;
fLanguage
English
Journal_Title
Power Systems, IEEE Transactions on
Publisher
ieee
ISSN
0885-8950
Type
jour
DOI
10.1109/59.221223
Filename
221223
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