Wu Leon Li author Columbia University. Computer Science Columbia University. Center for Computational Learning Systems Kaiser Gail E. author Columbia University. Computer Science Rudin Cynthia author Waltz David L. author Columbia University. Center for Computational Learning Systems Anderson Roger N. author Columbia University. Computer Science Columbia University. Earth and Environmental Sciences Boulanger Albert G. author Columbia University. Center for Computational Learning Systems Salleb-Aouissi Ansaf author Columbia University. Center for Computational Learning Systems Dutta Haimonti author Columbia University. Center for Computational Learning Systems Pooleery Manoj author Columbia University. Center for Computational Learning Systems Columbia University. Computer Science originator contributor text Technical reports New York Department of Computer Science, Columbia University 2011 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. Computer science CUCS-025-11 http://hdl.handle.net/10022/AC:P:13133 English NNC NNC 2012-05-03 16:42:29 -0400 2012-05-03 16:52:11 -0400 7136 eng