DocumentCode
908207
Title
Comparison of very short-term load forecasting techniques
Author
Liu, K. ; Subbarayan, S. ; Shoults, R.R. ; Manry, M.T. ; Kwan, C. ; Lewis, F.L. ; Naccarino, J.
Author_Institution
Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA
Volume
11
Issue
2
fYear
1996
fDate
5/1/1996 12:00:00 AM
Firstpage
877
Lastpage
882
Abstract
Three practical techniques-fuzzy logic (FL), neural networks (NN), and autoregressive models-for very short-term power system load forecasting are proposed and discussed in this paper. Their performances are evaluated through a computer simulation study. The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict very short-term power system load trends online. FL and NN can be good candidates for this application
Keywords
autoregressive processes; fuzzy logic; load forecasting; neural nets; power system analysis computing; autoregressive models; computer simulation; fuzzy logic; neural networks; performance evaluation; power systems; very short-term load forecasting; Application software; Computer simulation; Load forecasting; Logic; Neural networks; Performance evaluation; Power system dynamics; Power system modeling; Power system simulation; Predictive models;
fLanguage
English
Journal_Title
Power Systems, IEEE Transactions on
Publisher
ieee
ISSN
0885-8950
Type
jour
DOI
10.1109/59.496169
Filename
496169
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