Post on 04-Feb-2022
Unrestricted © Siemens AG 2019
April 2019Page 1 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
It’s not.Introducing Energy Efficiency Analytics,
the all inclusive Energy-Saving-as-a-Service (ESaaS).
Why is it so difficult to realize the full potential of
energy savings hidden in businesses across the world?
Green value from data:
State-of-the-art analytics for efficient
supply and use of energy
Hannover Messe 2019 | Integrated Energy Forum | April 2, 2019
Dr. Ralf Blumenthal | Siemens AG
siemens.com/already-esaasingUnrestricted © Siemens AG 2019
Unrestricted © Siemens AG 2019
April 2019Page 3 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Energy efficiency is not new – but more important than ever!
ISO 50001Energy
audits§62 EEG
(Germany)
ISO 50003DIN EN
16247
Dashboarding
Real-time
Cloud
BI
And now? How to get value out of it?
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April 2019Page 5 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Getting visibility onto process level at high frequency
is key to leverage the full potential of hidden energy savings
Robot
ERP
MES
SCADA
DCS
Big data
analytics
AI
Human
domain
expertise
EEA = Energy Saving
High-frequency sampling
Electricity
Gas
Water
Pump
EEA = Energy Saving as a Service
Additional energy savings and CO2
Better decision making
Improved process performance
101101101 101101101
1
0
1
0
0
0
1
Compressor
Transformer
Press
Furnace
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April 2019Page 6 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
EnergyIP Energy Efficiency Analytics works on a simple
principle – Turn the wealth of data in your facility into value
Massive DataAdditional insights with high-frequency sampling
down to 1 msec of energy consumption on asset level
Modern analyticsState-of-the-art big data and AI
algorithms for fast calculations
Renowned Expertise and Global FootprintDomain know-how, especially electrical,
built on strong legacy and global setup
Unmatched flexibilityModular approach tailored to your vertical,
specific needs and business environment
Save on reduced
energy consumption
and CO2 emissions
Reach better decisions and monetize
eco-certification
Improve process
performance across your
entire operations
Connect your assets to achieve continuously optimized business operations
various verticals with
no saving potential
0 customers across
energy reduction
Up to 25%
We
ISO 50001
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April 2019Page 8 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Why high-frequency monitoring so important?
Example: Low resolution @ 1 sample per 5 minutes
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April 2019Page 9 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Why high-frequency monitoring so important?
Example: High resolution @ 1 sample per 5 seconds
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April 2019Page 10 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Our horizontal bottom-up approach to energy efficiency is
applicable to various areas in industry and infrastructure
Selection of typically monitored assets Selected industry verticals
Manufacturing Airports Water &
waste
Chemical &
petro-
chemical
Pulp & paper Generation Oil & gas Buildings
AND MORE…
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April 2019Page 11 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Automotive
Creating measurable value in various areas of industry
and infrastructure – Selected customer examples
What is analyzed in this
customer example?
Thermosolar plant operation
Energy
Water waste and sludge
drying processes
Water and waste
Elevator usage, transformers
and heating
Building
Production processes
Example vertical?
Proven benefits for this
customer example? €200k savings in
auxiliary consumption per year
6% reduction in consumption
15%reduction in consumption
€8Msavings per year
7% reduction in consumption
Unrestricted © Siemens AG 2019
April 2019Page 12 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
First priority to create maximum value in minimum time –
Typical payback period of less than 2 years
Enablement of scaling across multiple sites through best-practice standardization
Maintenance cost reduction
through continuous monitoring of
smooth operations
Cost reduction
through reduced energy consumption
Process performance increase through
benchmarking and continuous optimization
Downtime reduction and avoidance
of breakdown cost through
continuous analysis of power quality
Failure reduction and equipment
lifetime increase through early
detection of defective operations
Decision support enhancement
through continuous high-quality
reporting in regular intervals
Monetized and certified CO2
reduction through continuous
monitoring with ISO 50001 certified tool
We ISO
50001
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April 2019Page 13 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Flexible and modular service offerings
that fit to your environment and your needs
Basic modules Expert modules
Service levels: Copper, Silver, GoldApplication modules: 4 Basic + 8 Expert
E1 | Reporting
engine
B2 | Real-time
web interface
B1 | Scalable
data acquisition
and management
system
B3 | Statistical
analysis
B4 | Alarm
engine
E8 | Auxiliary
system
control
E2 | Integration
of data from
control systems
(e.g. MES, DCS,
SCADA)
E3 | Artificial
Intelligence
modeling
E4 | Big data
analytics
interface
Recommendations-as-a-service
Analysis-as-a-service
Software-as-a-service
Gold
Tier
Silver
Tier
Copper
Tier
= Silver Tier
+ recommendations for potential
improvements as part of monthly reports
= Copper Tier
+ automated weekly and monthly reports
(configured and made to order)
= Full access to all functionalities of
real-time web interface and basic
modules
E5 | Live models E6 | Advanced
visualization
elements
E7 | Advanced
reporting
engine
Unrestricted © Siemens AG 2019
April 2019Page 14 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Identified efficiency measures stem from typical types of
analysis which address different main value drivers
No harmers
Quick
wins
Squeezers
Long
Game
changers
Short
Low HighInvestment
Payb
ack t
ime
Main value driversRough payback overview … … of analysis areas (selected, continuously adding)
High-frequency monitoring of energy consumption
Analysis and optimization of energy-based KPI’s per facility throughput
Active and reactive power analysis of transformers
Peak load analysis
Analysis of unnecessary loads and waste
A1
A2
A3
A4
A5
A1 A2
A8
A9
A4 A5A10
A3
A12
Decision support enhancement
Cost reduction
Electrical data Process data Auxiliary data Process Information
AI-based prediction of energy consumption and load patterns
Power quality analysis
Asset utilization analysis
AI-based optimization of operating parameters
Energy-based performance benchmarking
Analysis of unbalanced three-phase equipment and transformers
Anomaly detection and analysis of asset usage/load curve analysis
A7
A8
A9
A10
A11
A13
A14
Avoidance of breakdown cost
Process performance increase
Equipment lifetime increase
A11 A14
Required information:
Analysis of inefficient useA6
Analysis of process planning efficiencyA12
A6
A7A13
Unrestricted © Siemens AG 2019
April 2019Page 15 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Example for
Optimization of energy-based KPI’s per throughput
Time [2 weeks]
Power [kW]
Volume
flow [m³]
Description Result
• 5 air compressors generating required pressure and volume flow
for process
• ~150 MWh of electricity consumption per month
• Monitoring of overall specific energy consumption per volume flow
resulted in ability to define best configuration for compressors to
operate
• Reduced energy consumption
of 24%
• Equivalent to saving >400
MWh of electric energy per
year
Measures implemented
• Change of control parameters
Unrestricted © Siemens AG 2019
April 2019Page 16 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Example for
Asset utilization analysis
Description Result
• 1 air compressor
• An AI algorithm identified how an air compressor was operated
• Better knowledge and
traceability of how effectively
an asset is used
• Basis for performance
analysis leading to
maintenance efficiency
Measures implemented
• n/a
Green: Normal behavior. OK
Blue: Programmed stop. STOP
Red: Mal functioning behavior. BAD
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April 2019Page 17 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Example for
Energy-based performance benchmarking
Time [2 days]
Power [kW]
Description Result
• 2 presses at 0.9 MW nominal power each
• Presses produce same parts, but are manufactured by different
vendors
• Analysis showed that 1 press had higher consumption. The reason
was an unnoticed resetting of parameters
• Press consumption reduced
by 40%, leading to >140MWh
of energy savings per year
• Procurement team takes into
account energy consumption
into overall lifecycle costs of
an asset
Measures implemented
• Press vendor carried out
adjustments
Unrestricted © Siemens AG 2019
April 2019Page 18 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Example for Anomaly detection and analysis of asset
usage/load curve analysis
Time [14 hours]
Power
[kW]
Volume
flow [m³]
Description Result
• 4 water centrifuges at ~60kW nominal power each connected to 1
transformer
• Analysis showed that high peaks in the transformer load occurred
at times of high volume flow, although the loads of the centrifuges
remained constant
• Identification of unwanted
transformer behavior
detrimental to transformer
lifetime
Measures implemented
• Detailed root cause analysis
ongoing by customer
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April 2019Page 19 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Unrestricted © Siemens AG 2018
Page 19 Siemens Energy Management
100 million tonsof CO2 saved by 20291)
[= yearly emissions of world’s Top 5 airlines ]
Our vision for impact
Become part of it!
1) How? We are currently enabling our customers to save an average of 18.2 kg of CO2 per MB of data analyzed
Unrestricted © Siemens AG 2019
April 2019Page 20 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
Already ESaaSing?
Get your personal live assessment
of additional energy saving potential
hidden in your business.
Right here and now.
Hall 9 – booth D35.
siemens.com/already-esaasing
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April 2019Page 21 Dr. Ralf Blumenthal | Siemens Smart Infrastructure
We're happy to get in touch!
Dr. Ralf Blumenthal
Global Service Sales and Delivery
Mobile: +49 (173) 6961137
E-mail: ralf.blumenthal@siemens.com
Internet page:
www.siemens.com/already-esaasing