DocumentCode
1269353
Title
Integrated ANN approach to forecast load
Author
Swarup, K. Shanti ; Satish, B.
Author_Institution
Indian Inst. of Technol., Madras, India
Volume
15
Issue
2
fYear
2002
fDate
4/1/2002 12:00:00 AM
Firstpage
46
Lastpage
51
Abstract
The demand for electricity is known to vary by the time of the day, week, month, temperature, and usage habits of the consumers. Though usage habit is not directly observable, it may be implied in the patterns of usage that have occurred in the past. A short-term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. How well training data is chosen in an ANN is the defining factor in how well the network´s output will match the event being modeled.
Keywords
learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; ANN; assessment; dispatcher information; electricity demand; generation scheduling functions; integrated artificial neural network; power system security; short-term load-forecasting program; training data; usage habit; Economic forecasting; Fuel economy; Humidity; Input variables; Load forecasting; Load management; Power generation economics; Power system economics; Temperature; Weather forecasting;
fLanguage
English
Journal_Title
Computer Applications in Power, IEEE
Publisher
ieee
ISSN
0895-0156
Type
jour
DOI
10.1109/67.993760
Filename
993760
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