Neural networks - a tutorial for the power industry

Anoop Mathur, Tariq Samad

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Neural network computational models offer significant promise for improving several aspects of power plant operation. This paper provides a brief but comprehensive review of neural networks. Various learning paradigms are discussed, as is the use of neural networks for solving optimization problems. Several applications relevant to the utility industry are described briefly. These include load forecasting, security monitoring, turbine backpressure optimization and process modeling. These are applications in which the ability of neural networks to learn complex mappings from training data is used to develop estimators or predictors of various properties of interest. Characteristics that a problem should possess for a neural network approach to be viable are also discussed.

Original languageEnglish (US)
Pages (from-to)239-244
Number of pages6
JournalProceedings of the American Power Conference
StatePublished - Dec 1 1990


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