Predictive Analytics for Smart Grid
to
Make it happen
Britta Hilt | MD
Dublin, 6th May 2014
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IS Predict & Scheer Group
Employees
2010 - 2014
800
400
100
 Visionary, researcher and author of standard works for business
Turnover
(million €)
2010 - 2014
50
Prof. A.-W. Scheer
information systems
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 Member of the council for innovation and growth of the German
Government
 President of the German Association for Information Technology
(BITKOM 2007-2011)
 Ranked as 2nd most important German IT person (of 100) by
Computerwoche magazin (after Hasso Plattner / SAP) in 2011
 Founder of international software companies IDS Scheer & IMC
AG
 Sole Shareholder of Scheer Group GmbH
Locations
 Germany
 France
 Turkey
 Australia
 Great Britain
 Ukraine
 Austria
 Rumania
 Benelux
 Switzerland
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Smart Grid´s Complexities
Challenge 1 – How to…
Challenge 2 – How to…
… keep control in volatile grids?
… use energy efficiently?
 Various players
 High volatile power
generation & consumption
 On grid / Off grid
 Reliable & affordable
 “Green”
 …
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How to manage Smart Grid`s challenges
Plannable Energy Flows
Highly accurate knowledge when how
much
 Wind / sun energy will be generated
 Energy will be consumed by industry
and private households
Transparency on Root & Cause
Highly accurate knowledge which
(hidden) factors increase energy
consumption
 Complex human behavior
 Complex machine behavior
Challenge 1 – How to…
Challenge 2 – How to…
… keep control in volatile grids?
… use energy efficiently?
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Comparions of Prediction Tools
in Highly Volatile Use Cases
Comparison: 24 h prediction gas consumption
Example: Difficult month
Generic regression
Specific forecast
External supplier for utilities with focus energy
forecasts
Deviation
Average: 18%
Maximum: 47 %
Generic „Discovery“
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Resource Intelligence prediction with automatic
model generation
Deviation
Average: 8%
Maximum: 26 %
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Example Utilities
Gas Energy
Reduced costs for energy via more precise 24 h gas prediction
Objective:
Problem:
How:
Data:
Plan demand-oriented gas purchase for tomorrow & thus, reduce purchasing costs
Standard load profiles too inflexible for dynamic demand of consumer
Dynamic load profiles with flexible pattern recognizion
Historic gas consumptions, weather (past and forecast); no consumer classification
Resource Intelligence
Accuracy
O 24h (%)
Jan
Feb
Mar
Apr
May
Jun
96
89
91
88
86
88
April
State of the Art Solution
Accuracy
O 24h (%)
Jan
Feb
Mar
Apr
May
Jun
92
90
81
83
67
74
Resource Intelligence ca. twice as precise
than state of the art solution with standard load profiles
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Example Utilities
PV Power Usage
Reduced power costs due to optimal usage of own PV power
Objective: Run production machinery mostly on PV power, generated by your own
Problem: PV power very volatile and difficult to plan; energy demand of machinery also
volatile; energy demand does not match energy availability
How:
Foresighted machinery control via accurate PV power generation prediction
Data:
Weather (past / forecast) power generation (past)
Accurate 24 h PV power generation prediction
for 1 individual installation
Accuracy
Mar
Apr
May
Jun
Jul
Aug
O Month
O Day
94 %
91 %
97 %
93 %
94 % 93 % 99 % 96 %
92 % 93 % 95 % 95 %
Sep
Oct
97 % 92 %
93 % 93 %
Resource Intelligence realizes flexible and precise predictions despite high volatility
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Example Utilities
Water Consumption
Less costs for water supply
Objective: Cost optimization in drinking water supply
Problem: Water demand highly volatile and difficult to plan; Reduction of power costs
for pumps but also 100% availability of water supply
How:
Running pumps when power price is low thanks to precise water consumption
prediction
Data:
Weather (past / forecast), water consumption (past)
Accurate 24 h water consumption prediction
Accuracy
Jan
Feb
Mar
Apr
May
Jun
O Month
98,1 %
97,0 %
99,6 %
99,3 %
99,0 %
98,0 %
O Day
97 %
97 %
99 %
94 %
97 %
95 %
Resource Intelligence realizes flexible and precise predictions despite high volatility
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Example Production
Predictive Dispatching
Optimized Energy Dispatching
Objective:
Way:
Problem:
Data:
Efficient energy dispatching and planned energy purchase in steel company
Enable planning for large energy consumers despite “not planable” consumption
Highly volatile energy demand which does not seem to be caused by production.
Energy consumption & limited production (planning) data
Steel mass
Steel width
Required energy
Energy Demand
Variations in oven
Energy forecast for energy planning & efficiency analysis
1 month
Time: 24 h
Predicted Energy
Required Energy
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Machinery Control
Reduced Operating Costs
Energy consumption
Which factors cause increased energy consumption per tonne of pellets?
Objective: To control two wood dryer in optimal energy usage although product
quality stays the same
Please note: No permanent usage of dryers required.
Problem:
Highly varying energy consumption of wood dryer. Therefore, it is
unclear which factors increase energy consumption
Solution: Discovering influencing factors for energy consumption
via pattern recognizion and correlation analysis
Data:
Consumption of long distance heating, outside
temperature, other production data
1st Discovery
Despite expert expectation:
Air humidity has minimal influence on
energy consumption!
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2nd Discovery
More than proportional increase when
assembly belt speed is increased.
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Example: Predictive Maintenance
Individual demand-oriented maintenance via anomaly analysis
Objective:
Way:
Condition:
Problem:
Increase efficiency via early information on (future) wear & tear
Discover first and hidden signs when machinery does not run efficient anymore
Individual & cost-reduced analysis per machine without additional sensors
Strongly volatile energy demand, only engine energy data, no production data
Discovery of anomalies between 86% - 100%!
10 minutes: Engine run without disturbances
10 minutes: 51 disturbances due to breaks
Discover anomalies in machinery
behavior i.e. in resource consumption
Evaluate anomalies
Irregularities with various strengths and
frequency
Anomaly Details
No regularity in variable energy demand during disturbance
Early warning
Alert for technical service
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SAP CEO visits IS Predict
CeBIT 2014
SAP CEO
Jim Hagemann Snabe
informs himself about
Resource Intelligence
Projects
Increased efficiency for Man & Machinery
thanks to Predictive Control
The self-learning & adaptive IT system
for cost reduction
 23 % in Smart Home Grid
Realizing full potential of renewable energy usage
 16 % in Smart Utility
More precise energy purchase & sale, also for
renewable energy
 12 – 62 % in Smart Building
Foresighted and adaptive building energy control
 14 % in Smart Production
Resource management, energy dispatching,
machinery control, predictive maintenance,
process efficiency, capacity planning
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Honored with 8 Innovation Awards
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ff
Why we are here:
We want to improve Resource
Efficiency at Irish Grid /
Production / Power Plants, too.
We are looking for challenging
projects to optimize complex and
difficult processes with
innovative & self-learning IT
solutions.
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Contact:
Britta.Hilt@ispredict.com
+49 176 – 63 72 92 28
IS Predict GmbH
Scheer Tower | Uni Campus Nord D5.1
66123 Saarbrücken | Germany
Phone +49 681 – 96777-200
www.ispredict.com
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