Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description.

1. Interoperability Between Smart
and Legacy Devices in Energy
Management Systems
Networked and Embedded Systems
Wilfried Elmenreich | 2015-09-28
Workshop Energieinformatik
45. GI-Jahrestagung "Informatik, Energie und Umwelt"

2. Overview
• Home Energy Management System
Architecture
• Smart Devices
• Non-intrusive load monitoring for legacy
device integration
• Modeling device profiles and load models

3. Main reference
D. Egarter, A. Monacchi, T. Khatib, and W. Elmenreich.
Integration of legacy appliances into home energy
management systems. Journal of Ambient Intelligence and
Humanized Computing, 2015.
http://arxiv.org/pdf/1406.3252

4. Possible components of an (H)EMS
• Smart Meter
– Measures overall energy consumption in real time
• Smart Appliances
– Is able to communicate its power consumption, future
operation
– Can cooperatively switch off/on
• Legacy electric devices
• Gateway
– Interconnection/Interoperability
– Can run additional applications
• Human Computer Interface (HCI)

5. HEMS architecture
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Wilfried Elmenreich

6. Smart Appliances
• A smart appliance consists of
– a communication interface
– a local processing and decision unit
– the appliance's actual function
• Smart plug concept
– plug with measurement, control and
communication features
(+) Unified communication interface
(-) Missing knowledge about device condition
• Embedded intelligent control
– measurement, control and communication integrated with
device
(+) Device parameters (e.g., fridge temperature) can be
considered for control decisions
(-) Different implementations of data structures and access
Self-Organizing Smart Microgrids 6
Wilfried Elmenreich

7. Data Management
• Modeling
– Building information
– Building automation and description
– User information and preferences
– Energy management
– Weather models
– Sensors
• Interfaces and Query languages
– Semantic web mechanisms
– SPARQL Protocol and Query Language (SPARQL)
– C-SPARQL, SPARQLstream, EP-SPARQL, CQELS for dynamic
systems

8. Appliance and Description Model

9. Integration of Legacy Devices
• From data management view
– Device stub provides a unique
mapping of all devices
• How to provide input from device side?
– Smart applicance
– Smart outlet
– Legacy devices?

10. Power consumption as information
• A power draw is an information, e.g. on/off
– Aggregated power draw measured at smart meter
– Need to disaggregate power draws
 Non-Intrusive load monitoring

11. Non-Intrusive Load Monitoring
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Wilfried Elmenreich
P
t
P
t
P
t
P
t
P
t
P
t
Find out which com-
bination of power pro-
files match measured
power consumption.
P
P
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Measure the overall
power consumption
over time1.
2.

12. • Knapsack problem base approach
• NP-hard problem
• Used metaheuristic algorithms
• Evolutionary algorithm
• Differential Evolution
• Particle swarm optimization
• Firefly optimization
• Cuckoo search optimization
• Simulated annealing
Metaheuristic-based NILM

13. Particle Filter Based Load Disaggregation
PALDI
100
W
0W
300
W
0W
5W0W
1000
W
x1
t-1
x1
t x1
t+1
yt-1 yt yt+1
x2
t-1
x3
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Appliance model Fractional hidden markov model –
Household model
Aggregated power load

14. • Using a dataset of power draws from measurement campaign
• Dataset GREEND
• Households in Austria, Italy
• 1s measurement frequency, active power
• PALDI algorithm
• NILM based on FHMM
• Modeling device profiles and identification models
• Protégé tool
• Ontologie Web Language
Evaluation

15. Example of power readings from GREEND
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Wilfried Elmenreich

16. • Load disaggregation
• Ground truth vs. estimated
Results – Load identification

17. • Grouped appliances
Results – Multiple metering points

18. Results – Device Profile

19. Results – Load Identification Model

20. Summary
• The smart home energy management systems needs
– Smart appliances
– Automated device integration
– Support for legacy devices
• To support this, we provide
– Applications for saving energy
– NILM device detection
– Load identification based on machine-readable device descriptions
• Cost/benefit of home energy management system?
– Need for integration with other services (Ambient Assisted Living)
– Possible via applications operating on the same data and interfaces
• Case study shows load disaggregation and classification
• Future plan to integrate features into MJÖLNIR (open source HEMS)
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Wilfried Elmenreich

21. 22
Thank you very much for your attention!
Questions and
comments are welcome!
D. Egarter, A. Monacchi, T. Khatib, and W. Elmenreich. Integration
of legacy appliances into home energy management systems.
Journal of Ambient Intelligence and Humanized Computing, 2015.
http://arxiv.org/pdf/1406.3252

22. Further References
• Andrea Monacchi, Dominik Egarter, Wilfried Elmenreich,
Salvatore D'Alessandro, Andrea M. Tonello. GREEND: An
Energy Consumption Dataset of Households in Italy and
Austria. arXiv:1405.3100, 2014.
• D. Egarter and W. Elmenreich. EvoNILM - Evolutionary appliance detection for
miscellaneous household appliances. In Proc. of the Green and Efficient Energy
Applications of Genetic and Evolutionary Computation at the 2013 Genetic and
Evolutionary Computation Conference (GECCO 2013 GreenGEC). July 2013.
• A. Monacchi and W. Elmenreich. Insert-coin: turning the household into a prepaid
billing system. In Poster Abstract, 5th ACM Workshop On Embedded Systems For
Energy-Efficient Buildings. ACM, November 2013
• A. Monacchi, W. Elmenreich, Salvatore D'alessandro, and A. Tonello. Strategies for
domestic energy conservation in carinthia and friuli-venezia giulia. In Proceedings
of the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON
2013). IEEE, November 2013.
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Wilfried Elmenreich
http://www.monergy-project.eu/