Presentation is loading. Please wait.

Presentation is loading. Please wait.

MAKING CENTS OF ENERGY’S BIG DATA Big Data: A Success Story Brian Crow, PE CEO Verdeeco 1.

Similar presentations


Presentation on theme: "MAKING CENTS OF ENERGY’S BIG DATA Big Data: A Success Story Brian Crow, PE CEO Verdeeco 1."— Presentation transcript:

1 MAKING CENTS OF ENERGY’S BIG DATA Big Data: A Success Story Brian Crow, PE CEO Verdeeco 1

2 19902010 12 Reads 35,000 Reads Challenges in the New Utility World DATA EXPLOSION Usage Readings Demand Readings Alarms / Alerts Voltage Data Transformer Loading Outage Management Weather Stations Fault Current Indicators Distribution Automation New Technology, New Technology, New Technology……………. 2

3 3 DATA (Quantity) ALARMS ENCRYPTION HOURLY DATA STREET LIGHT CONTROL IPV6 CONFIGURATION POWER QUALITY SERVICE SWITCH / VALVE VOLTAGE TEMPERATURE PRESSURE ON-DEMAND READS OUTAGE DEMAND RESPONSE DISTRIBUTION AUTOMATION CVR FIRMWARE DOWNLOAD DATA ANALYTICS LOAD PROFILE 4-CHANNELS

4 0010110001010100111011010101001100100101100010101001110110101010011001 0110110010101001110110101010011001101101100101010011101101010100110011 1011000101010011101101010100110011110110001010100111011010101001100111 0110111010100110110101010011001110001101110101001101101010100110011100 0010110001010100111011010101001100100101100010101001110110101010011001 0110110010101001110110101010011001101101100101010011101101010100110011 1011000101010011101101010100110011110110001010100111011010101001100111 0110111010100110110101010011001110001101110101001101101010100110011100 0010110001010100111011010101001100100101100010101001110110101010011001 1011000101010011101101010100110011110110001010100111011010101001100111 0110111010100110110101010011001110001101110101001101101010100110011100 0010110001010100111011010101001100100101100010101001110110101010011001 0110110010101001110110101010011001101101100101010011101101010100110011 1011000101010011101101010100110011110110001010100111011010101001100111 0110111010100110110101010011001110001101110101001101101010100110011100 0010110001010100111011010101001100100101100010101001110110101010011001 35 Trillion New Data Points Data Storage Issues Increase in Data Silos Unchecked Load Back Office System Stress New Technology New Communications Analysis 4

5 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 SUBSTATIONS TRANSFORMERS METERS NETWORKS 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 0010110001010100111011010101001100111001000101100010101001110110101010011001110010 0110110010101001110110101010011001110010101101100101010011101101010100110011100101 1001000101010011101101010100110011100101010010001010100111011010101001100111001010 0110111010100110110101010011001110010101001101110101001101101010100110011100101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 011011101010011011101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 011011101010011011101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 011011101010011011101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 011011101010011011101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 011011101010011011101010 001011000101001011000101 011011001010011011001010 100100010101100100010101 5

6 Data is WORTHLESS, unless you turn it into information Storage Credibility Accessibility Query Report Alert Alarm ……………… 6

7 How do you start tackling the issue? 7

8 Review your technology options Cloud or in house? Hybrid Distributed or non-distributed Real time or non real-time Don’t let perfection stand in the way of progress! 8

9 Cloud Cloud is changing the technology world Innovation is rapidly being delivered Scalable Pilot projects can rapidly be deployed Fast Prototyping and testing Benchmark Hardware needs 9

10 Cloud Enables Diverse & Rapid Deployment Options 10

11 CLOUD COMPUTING Hardware Capacity and Costs Capacity Needs Time     Money left on the table with a fixed size data center   Inadequate capacity leads to slower processing with a fixed size data center Fixed Size Data Center Cloud Cloud enables us to define server profiles that mirror the performance of physical counterparts −RAM −CPU −Disk space Virtually unlimited bandwidth, pay for what you use No need to “build a church for Easter Sunday”

12 12 “As we've said“As we've said, the CIA's decision to choose Amazon over IBM is a significant moment in the evolution of the cloud services industry, and whether Amazon wins or not, the initial decision is indicative of a fundamental change in attitudes by government towards IT procurement.” Is the Cloud Safe?

13 13

14 AGGREGATE ANALYZE APPLY AGGREGATE ANALYZE APPLY Data Mash Up…Tear Down the Silo

15 DATA CREATES DATA, ANALYTICS CREATES… = Petabytes = More Analytics = More Data + Analytics Utility Data

16 What do you do with the data? 16 Predict Analyze Forecast Trend Benchmark Data Access

17 DATA ACCESS 17

18 18 Trend

19 Find some quick wins Make the data come alive Simple Reports Alerts and Alarms Operational Analysis 19

20 Customer Success Story 20 AMI Raw Data size:8 GB / Week 400 GB / Year -Does not include weather data Interval Length: Hourly Meters:Aprox 45k Transformers: Aprox 19K

21 Reports 21 Data Credibility Report: Jun 1- Aug 1 Estimations Worst Offender Report

22 Reports 22 Momentary Outages June 1- Aug 1 By meter

23 Data Benchmark & Trends 23 Momentary Outages

24 Trends 24

25 Analyze 25

26 26 436 Pages long 30 events / page 6540 transformers Never crossed 40% of rating for 7 days Analyze: Under Loaded Transformers

27 27 Transformer Load Forecasting

28 What Else? True Loss Analysis Theft & Revenue Protection Device Failure Capital Expenditures to Benefits Outage Analysis Unbilled Revenue Reporting 28

29 IT Involvement Get the data into the hands of the power users Find ways to monitor, but not have to continually update Spend time doing the things you are really good at and are highly valuable for your time and less time building out reports for someone else 29

30 Benefits and Value BENEFITS Quickly spot potential problem devices or areas through dashboards, geo- maps and drill down functionality Increase ROI on deployed hardware Cost-Effective Data Storage Cross functional system analysis (AMI vs SCADA) VALUE Don’t lose alarms, alerts and critical system data Improve Customer Relations Reduce Losses Identify issues and proactively manage assets Start-up quickly and improve performance faster Realize ROI in a short period of time 30

31 Grid Data and Cloud Computing Provides: The opportunity for Advanced metering data to be used not just stored The ability for the business to actively define how data can be used and processed. A turnkey hosted solution with a greatly accelerated ramp time for maximum use and return on your investment The ability for small to mid-sized Utilities to participate in the efficiencies of advanced metering data usage with minimal capital investment Integration to other data sources such as GIS, SCADA, CRM, Billing, etc. for cross validation and more efficient operations 31

32 32

33 Brian Crow, PE Brian.crow@verdeeco.com 404-831-3606 33 Thank You!


Download ppt "MAKING CENTS OF ENERGY’S BIG DATA Big Data: A Success Story Brian Crow, PE CEO Verdeeco 1."

Similar presentations


Ads by Google