Smart Meter Data Analytic using Hadoop

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Smart Meter Data Analytic using Hadoop

  1. 1. Smart Meter Data Analytic Using Hadoop Omkar Nibandhe and Abhishek Korpe students
  2. 2. SMART METER DATA ANALYTICS (SMDA) USING HADOOP By : Omkar Nibandhe ( Student ) Abhishek Korpe ( Student ) 03-04-2014SMDAusingHADOOP 2
  3. 3. Why Smart Meter Data Analytics ? 03-04-2014SMDAusingHADOOP 3
  4. 4. What are SMART METERS ? MIF • Track and store the amount of energy used. • Send the collected data to the Energy Distribution company server at regular time intervals. House/ Industry Smart Meter Server 03-04-2014SMDAusingHADOOP 4
  5. 5. Advantages • Service Provider • Demand-Response • Time of use tariff • Load Profile Analysis • Theft Detection • Billing Accuracy • Customers • Usage Pattern • Billing Accuracy • Convenience in change of service provider 03-04-2014SMDAusingHADOOP 5
  6. 6. Confronting the data deluge • Rate Generation – 15 minutes. • For single meter – 3000 readings/month (approx ). • For 1 million meters – 36 Billion readings/year (approx). • Annual Growth – 13% ( 2010 – 2015 ). • Total Shipment – 460.9 Million Smart Meters. Source: Build smart metering solutions with IBM Informix TimeSeries 03-04-2014SMDAusingHADOOP 6
  7. 7. Capitalizing on the unique value of Hadoop Solution ? • Reducing data load times. • Improving query performance. • Massive Scalability. 03-04-2014SMDAusingHADOOP 7
  8. 8. Demand - Response 03-04-2014SMDAusingHADOOP 8
  9. 9. Time of use Tariff 03-04-2014SMDAusingHADOOP 9
  10. 10. Load Profile Analysis Using hadoopUsing hadoop load time 03-04-2014SMDAusingHADOOP 10
  11. 11. Data Flow Diagram Predictive Analysis ( FLUME ) Load Profile Analysis 03-04-2014SMDAusingHADOOP 11
  12. 12. Hadoop Cluster in LAB 412 Masters Slaves Slave1 Slave7 Slave8 Slave9 Slave11 Slave6Slave5Slave4Slave3Slave2 Slave10 Slave19Slave18 Slave17Slave16Slave15Slave14Slave13 Slave12 Slave20 Slave21 03-04-2014SMDAusingHADOOP 12
  13. 13. LAB 412 (MESCOE Pune, India) 03-04-2014SMDAusingHADOOP 13
  14. 14. SMDA - NameNode 03-04-2014SMDAusingHADOOP 14
  15. 15. SMDA - SecondaryNameNode 03-04-2014SMDAusingHADOOP 15
  16. 16. SMDA - JobTracker 03-04-2014SMDAusingHADOOP 16
  17. 17. SMDA – Input ( .MIF ) 03-04-2014SMDAusingHADOOP 17 1392 19501 0.157
  18. 18. SMDA - Output 03-04-2014SMDAusingHADOOP 18
  19. 19. Test Job 1 03-04-2014SMDAusingHADOOP 19
  20. 20. Test Job 2 ( Combiner ) 03-04-2014SMDAusingHADOOP 20
  21. 21. Future Scope • Analysis of - • Customer segmentation. • Customer behavior. • Meter ping commands. • Outage management. • Power quality. • Extending data point(s) : weather, geographical location, family consumption, etc. 03-04-2014SMDAusingHADOOP 21
  22. 22. Thanks to …. • Irish Social Science Data Archive ( ISSDA ). • Rahul Khinvasara – Director, zCon Solutions Pvt. Ltd. • Modern Education Society’s College of Engineering (MESCOE). • Prof. Balaji Bodkhe – Guide, MESCOE. • Prof. N. Shaikh – Head of Computer Department, MESCOE. • Prof. A. Hake – Vice Principal, MESCOE. • Prof. P. Raut – Administrative Head, MESCOE. 03-04-2014SMDAusingHADOOP 22
  23. 23. Any Suggestions ?? 03-04-2014SMDAusingHADOOP 23
  24. 24. Thank You. 03-04-2014SMDAusingHADOOP 24
  25. 25. Any Questions? 03-04-2014SMDAusingHADOOP 25

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