Ranking Electrical Feeders of the New York City Power Grid Phil Gross Ansaf Salleb-Abouissi Haimonti Dutta Albert Boulanger Problem Primary electricity distribution cables (feeders,  in diagram), connecting substations to local transformers, fail regularly Successful prediction of which feeder is likely to fail next would have major benefits for both operations and maintenance of the electrical grid [1] Outage-Derived Data Sets (ODDS) are a mixture of feeder attribute snapshots from just before failures, labeled as +1, and snapshots of all feeders in their current state, labeled as -1 Ranking Methods Supervised Ranking Problem: Given a set of labeled examples, we would like a scoring hypothesis h that will rank all positive examples above all negative examples. We use Area under the ROC [2] to evaluate our ranking results. SVM: Svmlight, linear kernel; Output scores not thresholded, in order to generate ranking; Vapnik[3] Rankboost: Freund, Iyer, Schapire, Singer [4] MartiRank: Long and Servedio[5], Gross, et al. [6] Center for Computational Learning Systems, Columbia University Experiment Compare the effectiveness of several machine learning techniques for generating feeder rankings Focus is on summer performance, when electricity consumption is much higher due to air conditioner usage, and feeder failures far more frequent Each example has 237 attributes Models were trained over a fixed 45-day window previous to date under consideration Testing periods were 7 and 15 following date under consideration We want to test if feeders that actually failed were ranked high on the susceptibility list Results Rankboost and SVM Score Ranker have comparable results, while MartiRank has slightly worse performance Prediction over 7 days appears to be better than prediction over 15 days The system appears to exhibit concept drift, where the amount of historical data for optimal prediction occasionally plummets All algorithms agree on the time periods when such concept drifts occur Future Work : Use of multivariate feature selection mechanisms Better ways of aggregating time series data Integrating analysis of feeder subcomponents (cable sections, joints, transformers) Decision Tree-based ranking methods (Probability Estimate Trees (PETs) Tracking and Prediction of concept drift Estimation of feeder Time To Failure Recognition of imminent failure from dynamic data Comparative AUC Results Concept Drift: Optimal Window Sizes Examined 35 datasets of 7 days each Attempted to predict each set of outages using up to 6 previous datasets References [1] P. Gross, A. Salleb-Aouissi, H. Dutta, A. Boulanger. Susceptibility Ranking of Electrical Feeders: A Case Study. CCLS Tech Report CCLS-08-04, [2] A. P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145–1159, July [3] V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA, [4] Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. In ICML ’98: Proceedings of the Fifteenth International Conference on Machine Learning, pages 170–178, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. [5] P. M. Long and R. A. Servedio. Martingale boosting. In 18th Annual Conference on Learning Theory, Bertinoro, Italy, June 27-30, 2005, pages 79–94. Springer, [6] P. Gross et al. Predicting electricity distribution feeder failures using machine learning susceptibility analysis. In The Eighteenth Conference on Innovative Applications of Artificial Intelligence IAAI-06, Boston, Massachusetts, Acknowledgements Thanks to Maggie Chow, Arthur Kressner, Serena Lee, and many others at Consolidated Edison Company of New York, Charles Lawson at Digimedia, Marta Arias at Universitat Politècnica de Catalunya and David Waltz, Roger Anderson, Cynthia Rudin, Bert Huang, Hatim Diab, Sam Lee and Leon Wu at the Center for Computational Learning Systems, Columbia University, New York. Funding for this work is provided by a contract between Columbia University and the Consolidated Edison Company of New York. Summer Temperature Effect Data a mixture of static attributes describing cable structure and connections, and dynamic attributes describing the feeder’s environ- ment over time