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Simplify Accelerate Adapt Innovate
How HANA in-memory technologies
enable the insight on energy Miguel Gaspar Silva
Industry Director Utilities
Dirk Jan Boon
Project Manager - Alliander
© 2012 SAP AG. All rights reserved. 5
Imagine If
Customers Segmentation could be (real) energy data driven
Energy Energy Losses (Technical & Non-Technical) could be determined real time
Assets Could freely analyze and find correlations within all sensor data collected, optimize operations and extend the lifecycle of critical assets
Compliance Regulatory Reporting could be done on the fly
The In-memory Revolution:
Business Insight
360x Faster reporting
speed (BW)
460B Data records
analyzed in less
than a second
© 2012 SAP AG. All rights reserved. 7
Grid Infrastructure Analytics RDS:
Business process scope delivered
Two business processes
delivered:
1.Transformer Overload
Analysis: Retrospective
Overload Analysis
2.Transformer Overload
Analysis: Sensor Health
Analysis
1.Retrospective overload analysis allows
grid operators to maximize the lifetime of
transformers by analyzing overload data
and gaining insight into the loss of life of
the transformers.
2. Provides maintenance engineers,
the ability of to analyze sensor health
data from the grid.
Liander use case - Load Forecasting
8
Peak Load Determination
(HANA-based solution)
Load sensors
(2.2 Billion
records/ year)
Load Forecasting
Investment Planning
Load Forecasting – HANA-based solution
• Automated data
extraction
• Automatic outlier
detection
• Intuitive UI
9
© 2012 SAP AG. All rights reserved. 12
Pattern recognition as the basis for understanding
customers and their consumption behavior
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0 hrs 24 hrs
co
nsu
mp
tion
[kW
h]
To 438+ million daily profiles
In-memory pattern
recognition algorithm
crunches measured load
profiles into categorized
user behavior.
Technology # Meters # Daily Values in Seconds in Minutes in Hours in Days
STANDARD 1,200,000 438,000,000 527,285 8,788 146 6
HANA 1,200,000 438,000,000 674 11 0 0
Measured Profiles
Usage Patterns
* Measured and Projected to Average Customer Size
© 2012 SAP AG. All rights reserved. 13
In Memory Solutions at an Energy Retailer
HANA for Portfolio and Trading
System
Trading
Improved load forecasting
Load Analysis and financial reporting
Ad-hoc analysis
Wholesale Energy
Improved Settlement Reporting
Improved Portfolio Economic Risk
Analysis
Retail Energy
Improved Pricing
HANA for Customer Service
Provide online services for
customers to:
Understand energy usage
Energy Efficiency Benchmarking
Give tips and advice for energy savings
First Results:
Processes taking hours to days in legacy system
could be improved to seconds to minutes.
© 2012 SAP AG. All rights reserved. 14
Energy Settlement being managed in ISU
Time-Consuming
processes are
processed in HANA
Proof-of-Concept: Accelerated Energy Settlement powered by SAP In-Memory Technology
Start Settlement Select PODs for
settlement
Aggregate
consumption data for
selected PODs
Exception handling,
documentation, other
steps
Market
Communications
Settlement
workbench
SAP HANA
Settlement Data Schema
Settlement Functions
Joins, Aggregations, …
Replicate
Expected Results: Accelerate current process with millions of
interval meters by factors.
Accelerated daily,
weekly, and monthly
settlement processes
Enable ad-hoc
settlement
Perfect integration into
ISU standard process
to support market
communications and
audits
© 2012 SAP AG. All rights reserved. 15
• Considering only mutation, the genetic algorithm may change the downfall % parameter from features 2 and 3 in this pattern by 5% range to simulate the pattern results
• Feature 2 = 20 ranges
• Feature 3 = 20 ranges
• Total = 400 possibilities
• RI over conventional
database = 22 days
• RI over SAP HANA = 28
min
RI over SAP HANA allows
to improve detection of
new fraud patterns
Fraud Detection with HANA:
Revenue Intelligence by Choice
© 2012 SAP AG. All rights reserved. 16
Balance needed for features and use cases
End customer use cases Utility company use cases
Functions the utility would
select that is either
positive or neutral to
end customer
Functions the end customer
would select that is either
positive or neutral to the
utility
End customer and utility
have similar requirements
© 2012 SAP AG. All rights reserved. 18
Customer Energy Management functional overview
Customer Energy
Management
Benchmarking & Comparison (1.x) - Site Benchmarking
- Industry Benchmarking
- Customer goals
Smart Meter Visualization for multi sites - Show Consumption, CO2,
- Show different aggregation levels
(whole customer, site, meter)
Saving Monitoring (1.x) - Energy Saving
- CO2 goals
- Capacity Screen
Energy Services(1.x) - Alerting
- CO2 Service
- Energy Efficiency
Integration - Smart Meter Analytics
- CRM Systems
- Billing System
CEM API - Web Application embedded in a
Customer portal
- API for Native Mobile Applications
- API for Backend Process Integration
© 2012 SAP AG. All rights reserved. 20
Applications
Next generation SAP Real-time Data Platform
Example Architecture
“Real-time Utility Foundation” based on HANA and Sybase
SAP BW
SAP
for Utilities (ISU, CRM, EAM)
(Utility) Computing Engine Forecasting, Benchmarking, Aggregation, …
External Systems Marketing, Weather, Meter, etc.
Utility Data Schema
SMA
Replicate
BI Content ISU, CRM, SMA/CEM
CEM
Utility Business Functions Settlement, Bill-Shock, Demand Response, …
Partner
Demand & Supply
Forecasting
Process
Integration
Partner
* Validate RAP++ as future platform
Obrigado!
Email:
Industry Director Utilities
www.experiencehana.com