This document discusses using Hadoop for smart meter data analytics. Smart meters track energy usage and send data to utility servers. Analyzing large volumes of smart meter data presents challenges due to data growth rates. Hadoop can help by reducing data loads and improving query performance due to its scalability. The document outlines how Hadoop can enable demand response analysis, time of use tariff analysis, and load profile analysis. It also provides diagrams of a Hadoop cluster and data flow for smart meter data analytics.

1. Smart Meter Data Analytic Using
Hadoop
Omkar Nibandhe and Abhishek Korpe
students

2. SMART METER
DATA ANALYTICS
(SMDA)
USING HADOOP
By :
Omkar Nibandhe ( Student )
Abhishek Korpe ( Student )
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3. Why Smart Meter Data
Analytics ?
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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
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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
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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
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7. Capitalizing on the unique
value of Hadoop
Solution ?
• Reducing data load times.
• Improving query performance.
• Massive Scalability.
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8. Demand - Response
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9. Time of use Tariff
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10. Load Profile Analysis
Using hadoopUsing hadoop
load
time
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11. Data Flow Diagram
Predictive
Analysis
( FLUME )
Load Profile
Analysis
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12. Hadoop Cluster in LAB 412
Masters
Slaves
Slave1
Slave7 Slave8 Slave9 Slave11
Slave6Slave5Slave4Slave3Slave2
Slave10
Slave19Slave18
Slave17Slave16Slave15Slave14Slave13
Slave12
Slave20 Slave21
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13. LAB 412 (MESCOE Pune, India)
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14. SMDA - NameNode
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15. SMDA - SecondaryNameNode
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16. SMDA - JobTracker
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17. SMDA – Input ( .MIF )
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1392 19501 0.157

18. SMDA - Output
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19. Test Job 1
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20. Test Job 2 ( Combiner )
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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.
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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.
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23. Any Suggestions ??
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24. Thank You.
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25. Any Questions?
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