Talk at the 13th Workshop on Intelligent Solutions in Embedded Systems (WISES), 2017 Venue: Helmut Schmidt University of the Federal Armed Forces Hamburg

1. YaY - An Open-Hardware Energy
Measurement System
WISES 2017
Christoph Klemenjak and Wilfried Elmenreich
Institute of Networked and Embedded Systems
Alpen-Adria-Universit¨at Klagenfurt
12.06.2017
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2. Chair of Smart Grids
• Self-organising
Systems
• Smart
Microgrids
• Energy
Informatics
• NIALM
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3. Introduction
• Changing housing situations, demographical developments in
our society, and the introduction of Smart Microgrids
• Analyse user behaviour, human biorhythm, consumption
habits, presence at home
• Data of interest is the power consumption of day-to-day used
household appliances
• Measurement system required to record and identify
alteration
Motivation
I Reduce costs
I Predict the energy consumption
I Optimise workflows and schedules
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4. A Distributed Measurement System
(a) YoMo Smart Meter-
ing Board [1]
(b) Smart Plug (c) Sensor Node
Figure: Components of the distributed measurement system
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5. A Distributed Measurement System
• Provide a detailed overview of the household/building
• Smart meter at the feed point of the household:
I RMS value of the voltage URMS
I RMS value of the current IRMS
I Active power P, reactive power Q, and apparent power S
• Exclusive characteristics on appliance level:
I Active power P
I Ambient features: Heat-dissipation, light emission, vibrations,
or sound emission
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6. YaY Smart Meter
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7. YaY Smart Meter
• Integrates a self-designed Smart Metering board
• Arduino board comprises two processors
• DIN rail enclosure for installation in the distribution board
• Communication interfaces: Wi-Fi and ZigBee
YoMo
Board
Microcontroller
Embedded
Processor
Wi-Fi
Module
Wi-Fi
Module
ZigBee
Module
YaY Smart Meter
Arduino Yun
Figure: Components of the YaY smart meter
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8. YaY Smart Meter: Measurement Aspects
Feature Abbreviation Value
Supply voltage Vdd 5 V
Max. current I 20 A
Max. input voltage V 400 V
Max. sampling frequency fs 5 Hz
Max. measurement error P 3.8%
Max. measurement error S 4.04%
Power consumption P 2 W
Table: YoMo Metering board specifications
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9. Smart Plugs: Measurement Aspects
• Measure power consumption of individual appliances
• Precise appliance patterns can be generated
Feature Abbreviation Value
Supply voltage Vac 230 V
Max. current I 16 A
Max. input voltage Vin 240 V
Max. sampling frequency fs 1 Hz
Measurement accuracy P 5%
Power consumption P 0.55 W
Table: Plugwise smart plugs specifications
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10. Ambient Appliance Features
• Integration of environmental networked sensors and wireless
sensor networks
• Monitor the impact of appliances on their environment:
I Piezoelectric sensors to record vibrations
I Temperature sensors to measure the temperature flow in the
surrounding of the respective appliance
I Sound sensors to measure characteristic noise
I Luminance sensors allow the detection of light-emitting
appliances such as lighting or television sets
I Gyroscope sensors can be applied to track motions
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11. Area of Application
1 Personalised feedback in form of appliance-specific
recommendations can be generated in order to achieve energy
savings
2 Generate and utilise appliance models to detect active
appliances
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12. Application as Energy Advisor Tool
Figure: Real-Time Widget
Figure: Advisor Widget[2]
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13. Load Disaggregation / Appliance Detection
Figure: The load disaggregation problem
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14. 0 1000 2000 3000 4000 5000 6000 7000 8000
0
1000
2000
Time in s
PowerinW
(a) Power consumption of a washing machine over time
0 1000 2000 3000 4000 5000 6000 7000 8000
0
500
1000
1500
2000
Time in s
PowerinW
(b) Power consumption of a dishwasher over time
Figure: Example detections of a refrigeratorChristoph Klemenjak and Wilfried Elmenreich YaY - An Open-Hardware Energy Measurement System
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15. Discussion
• Measurement Aspects:
I YaY Smart Meter at the feed point
I Smart plugs attached to selected appliances
I Networked sensors across the household
• Expandable open-hardware platform
• Open access
• Customisable system
Future Work
• Optimal utilisation of appliance models to detect ageing
e↵ects and irregularities
• Detect such e↵ects and advice the user to act
• Predict maintenance
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16. Conclusion
• A novel open-source and open-hardware metering approach
• Record and monitor electrical and ambient features of
electrical appliances
• In combination with diagnosis software (energy advisors) such
systems could present a low-cost alternative to expensive
commercial home monitoring systems
• Transition from smart to cognitive
• Achieve significant energy savings by the identification of
energy-sapping appliances and the recognition of ageing
e↵ects of electrical appliances
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17. References
Christoph Klemenjak, Dominik Egarter, and Wilfried
Elmenreich.
Yomo: the arduino-based smart metering board.
Computer Science-Research and Development,
31(1-2):97–103, 2016.
Andrea Monacchi, Fabio Versolatto, Manuel Herold, Dominik
Egarter, Andrea M Tonello, and Wilfried Elmenreich.
An open solution to provide personalized feedback for building
energy management.
Journal of Ambient Intelligence and Smart Environments,
9(2):147–162, 2017.
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