Data quality assurance and performance measurement of data mining for preventive maintenance of power grid
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Rudin_Data quality.pdf
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902.03 KB
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Adobe PDF
Checksum (MD5)
747d2b5c0cea336fd139f977b849a1bd
Author(s)
Wu, Leon
Kaiser, Gail
Rudin, Cynthia
Anderson, Roger
Date Issued
2011
Journal
Proceedings of the First International Workshop on Data Mining for Service and Maintenance (KDD4Service '11)
Publisher
Association for Computing Machinery (ACM)
Citation
Leon Wu, Gail Kaiser, Cynthia Rudin, and Roger Anderson. 2011. "Data quality assurance and performance measurement of data mining for preventive maintenance of power grid." In Proceedings of the First International Workshop on Data Mining for Service and Maintenance (KDD4Service '11). ACM, New York, NY, USA, 28-32.
Version
Author's final manuscript
Abstract
Ensuring reliability as the electrical grid morphs into the "smart grid" will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance; in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning and data mining) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning and data mining algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning and data mining results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning and data mining systems effectively and efficiently.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2018673.2018679
