Machine Learning for the New York City Power Grid

  title={Machine Learning for the New York City Power Grid},
  author={Cynthia Rudin and David L. Waltz and Roger Anderson and Albert Boulanger and Ansaf Salleb-Aouissi and Maggie L Chow and Haimonti Dutta and Philip Gross and Bert Huang and Steve Ierome},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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 models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2… CONTINUE READING
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