Machine Learning for the New York City Power Grid

Machine Learning for the New York City Power Grid

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These...

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Bibliographic Details
Main Authors: Rudin, Cynthia (Contributor), Waltz, David (Contributor), Anderson, Roger N. (Author), Boulanger, Albert (Author), Salleb-Aouissi, Ansaf (Author), Chow, Maggie (Author), Dutta, Haimonti (Author), Gross, Philip N. (Author), Huang, Bert (Author), Ierome, Steve (Author), Isaac, Delfina F. (Author), Kressner, Arthur (Author), Passonneau, Rebecca J. (Author), Radeva, Axinia (Author), Wu, Leon (Author)
Other Authors: Sloan School of Management (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers, 2012-01-23T18:08:45Z.
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