Simplify Accelerate Adapt Innovate - engerati.com Gaspar Silva.pdf · Simplify Accelerate Adapt...

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


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


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


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.


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


• 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


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


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


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

What happens when innovation takes place?

Obrigado!

Email:

[email protected]

Industry Director Utilities

www.experiencehana.com

mailto:[email protected]

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