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The Data Tsunami for AGL Energy

 The Data Tsunami for AGL Energy - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Utilities
Use Cases
  • Advanced Metering Infrastructure
The Challenge
AGL Energy, one of Australia’s largest power retailers, is replacing its electromechanical meters with interactive smart meters in 2.2 million homes and 300,000 businesses throughout the state of Victoria, including its capital, Melbourne. This is nearly a 4,320-fold increase in daily data processing for electricity providers. It’s also the start of a “data tsunami,” swelling up from these 2.5 million smart meters and flowing into AGL Energy’s billing and operations systems. AGL’s Business Requirements - For wholesale energy: Improved settlement reporting - For merchant energy portfolio management: Better load forecasting, accuracy and data analytics / segmentation with better reporting - For analytics: Better forecasting and reporting
The Customer
AGL Energy
About The Customer
AGL Energy
The Solution
The company turned to TCS to introduce new tools and databases to lay the foundation for a broader spectrum of its business units to access the new data - thus enabling AGL Energy to work smarter in forecasting and data analytics to improve business performance. To accomplish this, AGL Energy and TCS selected SAP HANA - together with new analytical capabilities - which are being developed in 14 “sprints” using AGILE software development methodology.

The AGL Energy team and TCS team collaborated with the TCS EntSol SAP CoE team to build the first sprint on the TCS large SAP HANA appliance environment housed at its SAP Global Center of Excellence supporting SAP HANA located in Cincinnati.
Operational Impact
  • [Data Management - Data Integration]
    Focused business decisions across merchant, retail and corporate business areas
  • [Cost Reduction - Operation]
    Greater certainty in financial reporting and load forecasting. Reduced operational and hedging costs. Certainty over financial outcomes
  • [Data Management - Data Accuracy]
    Single source of truth for all data
Quantitative Benefit
  • 50 times faster load forecasting (from 5 weeks to 4 hours)
  • 5,000% faster portfolio aggregation (from 50 hours to 1 hour)
  • 60 times faster settlement reconciliation (60 minutes to less than 1 minute)

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