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Real-time Ranking of Electrical Feeders using Expert Advice Hila Becker  1,2 , Marta Arias  1 1 Center for Computational L...
Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data  </li><...
The Electrical System 1. Generation 2. Transmission 3. Primary Distribution 4. Secondary Distribution
The Problem <ul><li>Distribution feeder failures result in automatic feeder  shutdown </li></ul><ul><ul><li>called “Open A...
Some facts about feeders and failures <ul><li>mostly 0-5 failures per day </li></ul><ul><li>more in the summer </li></ul><...
Feeder data <ul><li>Static  data </li></ul><ul><ul><li>Compositional/structural </li></ul></ul><ul><ul><li>Electrical </li...
Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data  </li><...
Machine Learning Approach <ul><li>Leverage Con Edison’s domain knowledge and resources </li></ul><ul><li>Learn to rank fee...
Feeder  Ranking Application <ul><li>Goal:  rank  feeders according to failure susceptibility   </li></ul><ul><ul><li>High ...
Application Structure Static data SQL Server DB ML Engine ML Models Rankings Decision Support GUI Action Driver Action Tra...
Decision Support GUI
Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data  </li><...
Simple Solution <ul><li>Supervised batch-learning algorithms </li></ul><ul><ul><li>Use past data to train a model </li></u...
Performance Metric <ul><li>Normalized  average rank of failed feeders </li></ul>
Performance Metric Example ranking outages 0 8 0 7 0 6 1 5 0 4 1 3 1 2 0 1
Real-time ranking with MartiRank <ul><li>MartiRank is a “ batch ” learning algorithm </li></ul><ul><li>Deal with changing ...
Real-time ranking with MartiRank time time time
How to measure performance over time <ul><li>Every ~15 minutes, generate new ranking based on current model and latest dat...
Using MartiRank for real-time ranking of feeders <ul><li>MartiRank seems to work well, but.. </li></ul><ul><ul><li>User de...
Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data  </li><...
Learning from expert advice <ul><li>Consider each model as an expert </li></ul><ul><li>Each expert has associated weight <...
Learning from expert advice <ul><li>Advantages </li></ul><ul><ul><li>Fully automatic </li></ul></ul><ul><ul><li>Adaptive <...
Weighted Majority Algorithm  [Littlestone & Warmuth ‘88] <ul><li>Introduced for binary classification </li></ul><ul><ul><l...
Weighted Majority Algorithm  e 1 . . . e 2 e 3 e N N Experts w 1 w 2 w 3 w N 1 0 0 1 ? w 1 *1 + w 2 *0 + w 3 *0 +  . . .  ...
In our case, can’t use WM directly <ul><li>Use ranking as opposed to binary classification </li></ul><ul><li>More importan...
Dealing with ranking vs. binary classification <ul><li>Ranking loss as normalized average rank of failures as seen before,...
Dealing with a moving set of experts <ul><li>Introduce new parameters </li></ul><ul><ul><li>B: “budget” (max number of mod...
Online Ranking Algorithm  e 1 . . . e 2 e 3 e B w 1 w 2 w 3 w B ? F1 F4 F3 F2 F5 F4 F2 F1 F3 F5 F1 F3 F5 F4 F2 F1 F3 F4 F2...
Other parameters <ul><li>How often do we train and add new models? </li></ul><ul><ul><li>Hand-tuned over the course of the...
Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data  </li><...
Experimental Comparison <ul><li>Compare our approach to </li></ul><ul><ul><li>Using “batch”-trained models </li></ul></ul>...
Performance – Summer 2005
Performance – Winter 2006
Parameter Variation - Budget
Future Work <ul><li>Concept drift detection </li></ul><ul><ul><li>Add new models only when change is detected </li></ul></...
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Real-time Ranking of Electrical Feeders using Expert Advice

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Hila Becker, Marta Arias, "Real-time Ranking of Electrical Feeders using Expert Advice", European Workshop on Data Stream Analysis 2007

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  • Generation: a prime mover, typically the force of water, steam, or hot gasses on a turbine, spins an electromagnet, generating large amounts of electrical current at a generating station Transmission: the current is sent at very high voltage (hundreds of thousands of volts) from the generating station to substations closer to the customers Primary Distribution: electricity is sent at mid-level voltage (tens of thousands of volts) from substations to local transformers , over cables called feeders, usually 10-20 km long, and with a few tens of transformers per feeder. Feeders are composed of many feeder sections connected by joints and splices Secondary Distribution: sends electricity at normal household voltages from local transformers to individual customers
  • Many occur in the summer when the load on the system increases, during heat waves When an O/A occurs, the load that had been carried by the failed feeder must shift to adjacent feeders, further stressing them. This can lead to a failure cascade in a distribution network
  • (e.g. age and composition of each feeder section) and e.g. electrical load data for a feeder and its transformers, accumulating at a rate of several hundred megabytes per day) Software engineering challenges to manage data
  • with sufficient accuracy so that timely preventive maintenance can be taken on the right feeders at the right time.
  • How to deal with impending failures that are corrected on the basis of our ranking
  • Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.) Re-train daily, or weekly, or every 2 weeks, or monthly, or…
  • Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.) Re-train daily, or weekly, or every 2 weeks, or monthly, or…
  • No human intervention needed Changes in system are learned as it runs
  • Every 7 days Seems to achieve balance of generating new models to adapt to changing conditions without overflowing system
  • if a model was successful in the previous summer but was retired during the winter, it should be rescued back in the upcoming summer if similar environmental conditions reappear.
  • Transcript of "Real-time Ranking of Electrical Feeders using Expert Advice"

    1. 1. Real-time Ranking of Electrical Feeders using Expert Advice Hila Becker 1,2 , Marta Arias 1 1 Center for Computational Learning Systems 2 Computer Science Department Columbia University
    2. 2. Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data </li></ul></ul><ul><li>Approach </li></ul><ul><li>Challenges </li></ul><ul><li>Our solution using Online learning </li></ul><ul><li>Experimental results </li></ul>
    3. 3. The Electrical System 1. Generation 2. Transmission 3. Primary Distribution 4. Secondary Distribution
    4. 4. The Problem <ul><li>Distribution feeder failures result in automatic feeder shutdown </li></ul><ul><ul><li>called “Open Autos” or O/As </li></ul></ul><ul><li>O/As stress networks, control centers, and field crews </li></ul><ul><li>O/As are expensive ($ millions annually) </li></ul><ul><li>Proactive replacement is much cheaper and safer than reactive repair </li></ul><ul><li>How do we know which feeders to fix? </li></ul>
    5. 5. Some facts about feeders and failures <ul><li>mostly 0-5 failures per day </li></ul><ul><li>more in the summer </li></ul><ul><li>strong seasonality effects </li></ul>
    6. 6. Feeder data <ul><li>Static data </li></ul><ul><ul><li>Compositional/structural </li></ul></ul><ul><ul><li>Electrical </li></ul></ul><ul><li>Dynamic data </li></ul><ul><ul><li>Outage history (updated daily) </li></ul></ul><ul><ul><li>Load measurements (updated every 15 minutes) </li></ul></ul><ul><li>Roughly 200 attributes for each feeder </li></ul><ul><ul><li>New ones are still being added. </li></ul></ul>
    7. 7. Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data </li></ul></ul><ul><li>Approach </li></ul><ul><li>Challenges </li></ul><ul><li>Our solution using Online learning </li></ul><ul><li>Experimental results </li></ul>
    8. 8. Machine Learning Approach <ul><li>Leverage Con Edison’s domain knowledge and resources </li></ul><ul><li>Learn to rank feeders based on failure susceptibility </li></ul><ul><li>How? </li></ul><ul><ul><li>Assemble data </li></ul></ul><ul><ul><li>Train ranking model based on past data </li></ul></ul><ul><ul><li>Re-rank frequently using model on current data </li></ul></ul>
    9. 9. Feeder Ranking Application <ul><li>Goal: rank feeders according to failure susceptibility </li></ul><ul><ul><li>High risk placed near the top </li></ul></ul><ul><li>Integrate different types of data </li></ul><ul><li>Interface that reflects the latest state of the system </li></ul><ul><ul><li>Update feeder ranking every 15 min. </li></ul></ul>
    10. 10. Application Structure Static data SQL Server DB ML Engine ML Models Rankings Decision Support GUI Action Driver Action Tracker Decision Support App Outage data Xfmr Stress data Feeder Load data
    11. 11. Decision Support GUI
    12. 12. Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data </li></ul></ul><ul><li>Approach </li></ul><ul><li>Challenges </li></ul><ul><li>Our solution using Online learning </li></ul><ul><li>Experimental results </li></ul>
    13. 13. Simple Solution <ul><li>Supervised batch-learning algorithms </li></ul><ul><ul><li>Use past data to train a model </li></ul></ul><ul><ul><li>Re-rank frequently using this model on current data </li></ul></ul><ul><li>Use the best performing learning algorithm </li></ul><ul><ul><li>How do we measure performance? </li></ul></ul><ul><ul><li>MartiRank - boosting algorithm by [Long & Servedio, 2005] </li></ul></ul><ul><ul><ul><li>Use MartiRank for dynamic feeder ranking </li></ul></ul></ul>
    14. 14. Performance Metric <ul><li>Normalized average rank of failed feeders </li></ul>
    15. 15. Performance Metric Example ranking outages 0 8 0 7 0 6 1 5 0 4 1 3 1 2 0 1
    16. 16. Real-time ranking with MartiRank <ul><li>MartiRank is a “ batch ” learning algorithm </li></ul><ul><li>Deal with changing system by: </li></ul><ul><ul><li>generating new datasets with latest data </li></ul></ul><ul><ul><li>Re-training new model, replacing old model </li></ul></ul><ul><ul><li>Using newest model to generate ranking </li></ul></ul><ul><li>Must implement “training strategies” </li></ul>
    17. 17. Real-time ranking with MartiRank time time time
    18. 18. How to measure performance over time <ul><li>Every ~15 minutes, generate new ranking based on current model and latest data </li></ul><ul><li>Whenever there is a failure, look up its rank in the latest ranking before the failure </li></ul><ul><li>After a whole day, compute normalized average rank </li></ul>
    19. 19. Using MartiRank for real-time ranking of feeders <ul><li>MartiRank seems to work well, but.. </li></ul><ul><ul><li>User decides when to re-train </li></ul></ul><ul><ul><li>User decides how much data to use for re-training </li></ul></ul><ul><ul><li>Performance degrades over time </li></ul></ul><ul><li>Want to make system automatic </li></ul><ul><ul><li>Do not discard all old models </li></ul></ul><ul><ul><li>Let the system decide which models to use </li></ul></ul>
    20. 20. Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data </li></ul></ul><ul><li>Approach </li></ul><ul><li>Challenges </li></ul><ul><li>Our solution using Online learning </li></ul><ul><li>Experimental results </li></ul>
    21. 21. Learning from expert advice <ul><li>Consider each model as an expert </li></ul><ul><li>Each expert has associated weight </li></ul><ul><ul><li>Reward/penalize experts with good/bad predictions </li></ul></ul><ul><ul><li>Weight is a measure of confidence in expert’s prediction </li></ul></ul><ul><li>Predict using weighted average of top-scoring experts </li></ul>
    22. 22. Learning from expert advice <ul><li>Advantages </li></ul><ul><ul><li>Fully automatic </li></ul></ul><ul><ul><li>Adaptive </li></ul></ul><ul><ul><li>Can use many types of underlying learning algorithms </li></ul></ul><ul><ul><li>Good performance guarantees from learning theory: performance never too far off from best expert in hindsight </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Computational cost: need to track many models “in parallel” </li></ul></ul>
    23. 23. Weighted Majority Algorithm [Littlestone & Warmuth ‘88] <ul><li>Introduced for binary classification </li></ul><ul><ul><li>Experts make predictions in [0,1] </li></ul></ul><ul><ul><li>Obtain losses in [0,1] </li></ul></ul><ul><li>Pseudocode: </li></ul><ul><ul><li>Learning rate as main parameter, ß in (0,1] </li></ul></ul><ul><ul><li>There are N “experts”, initially weight is 1 for all </li></ul></ul><ul><ul><li>For t=1,2,3, … </li></ul></ul><ul><ul><ul><li>Predict using weighted average of experts’ prediction </li></ul></ul></ul><ul><ul><ul><li>Obtain “true” label; each expert incurs loss l i </li></ul></ul></ul><ul><ul><ul><li>Update experts’ weights using w i,t+1 = w i,t • pow(ß,l i ) </li></ul></ul></ul>
    24. 24. Weighted Majority Algorithm e 1 . . . e 2 e 3 e N N Experts w 1 w 2 w 3 w N 1 0 0 1 ? w 1 *1 + w 2 *0 + w 3 *0 + . . . + w N *1 >0.5 <0.5 1 0 1 <ul><li>Calculate li = loss(i) for i=1,…,N </li></ul><ul><li>Update: wi,t+1 = wi,t • pow(ß,li) for learning rate ß, time t and i=1,..,N </li></ul>
    25. 25. In our case, can’t use WM directly <ul><li>Use ranking as opposed to binary classification </li></ul><ul><li>More importantly, do not have a fixed set of experts </li></ul>
    26. 26. Dealing with ranking vs. binary classification <ul><li>Ranking loss as normalized average rank of failures as seen before, loss in [0,1] </li></ul><ul><li>To combine rankings, use a weighted average of feeders’ ranks </li></ul>
    27. 27. Dealing with a moving set of experts <ul><li>Introduce new parameters </li></ul><ul><ul><li>B: “budget” (max number of models) set to 100 </li></ul></ul><ul><ul><li>p: new models weight percentile in [0,100] </li></ul></ul><ul><ul><li> : age penalty in (0,1] </li></ul></ul><ul><li>If too many models (more than B), drop models with poor q-score, where </li></ul><ul><ul><li>q i = w i • pow(  , age i ) </li></ul></ul><ul><ul><li>I.e.,  is rate of exponential decay </li></ul></ul>
    28. 28. Online Ranking Algorithm e 1 . . . e 2 e 3 e B w 1 w 2 w 3 w B ? F1 F4 F3 F2 F5 F4 F2 F1 F3 F5 F1 F3 F5 F4 F2 F1 F3 F4 F2 F5 F1 F3 F4 F2 F5 F3 F1 F4 F2 F5 e B+1 e B+2 w B+1 w B+2
    29. 29. Other parameters <ul><li>How often do we train and add new models? </li></ul><ul><ul><li>Hand-tuned over the course of the summer </li></ul></ul><ul><ul><li>Alternatively, one could train when observed performance drops .. not used yet </li></ul></ul><ul><li>How much data do we use to train models? </li></ul><ul><ul><li>Based on observed performance and early experiments </li></ul></ul><ul><ul><ul><li>1 week worth of data, and </li></ul></ul></ul><ul><ul><ul><li>2 weeks worth of data </li></ul></ul></ul>
    30. 30. Overview <ul><li>The problem </li></ul><ul><ul><li>The electrical system </li></ul></ul><ul><ul><li>Available data </li></ul></ul><ul><li>Approach </li></ul><ul><li>Challenges </li></ul><ul><li>Our solution using Online learning </li></ul><ul><li>Experimental results </li></ul>
    31. 31. Experimental Comparison <ul><li>Compare our approach to </li></ul><ul><ul><li>Using “batch”-trained models </li></ul></ul><ul><ul><li>Other online learning methods </li></ul></ul><ul><li>Ranking Perceptron </li></ul><ul><ul><li>Online version </li></ul></ul><ul><li>Hand Picked Model </li></ul><ul><ul><li>Tuned by humans with domain knowledge </li></ul></ul>
    32. 32. Performance – Summer 2005
    33. 33. Performance – Winter 2006
    34. 34. Parameter Variation - Budget
    35. 35. Future Work <ul><li>Concept drift detection </li></ul><ul><ul><li>Add new models only when change is detected </li></ul></ul><ul><li>Ensemble diversity control </li></ul><ul><li>Exploit re-occurring contexts </li></ul>
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