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Real-time Ranking of Electrical Feeders using Expert Hila Becker1

Summary: Real-time Ranking of Electrical Feeders using Expert
Hila Becker1
, Marta Arias2
Computer Science, Columbia University, New York
Center for Computational Learning Systems, Columbia University, New York
Abstract. We are using machine learning to construct a failure-susceptibility ranking of
feeders that supply electricity to the boroughs of New York City. The electricity system is
inherently dynamic and driven by environmental conditions and other unpredictable factors,
and thus the ability to cope with concept drift in real time is central to our solution. Our
approach builds on the ensemble-based notion of learning from expert advice as formulated
in the continuous version of the Weighted Majority algorithm [16]. Our method is able to
adapt to a changing environment by periodically building and adding new machine learning
models (or "experts") based on the latest data, and letting the online learning framework
choose what experts to use as predictors based on recent performance. Our system is cur-
rently deployed and being tested by New York City's electricity distribution company.
Keywords: Concept Drift, Online Learning, Weighted Majority Algorithm, Rank-


Source: Arias, Marta - Departament of Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya


Collections: Computer Technologies and Information Sciences