Interoperability Between Smart
and Legacy Devices in Energy
Management Systems
Networked and Embedded Systems
Wilfried Elm...
Overview
• Home Energy Management System
Architecture
• Smart Devices
• Non-intrusive load monitoring for legacy
device in...
Main reference
D. Egarter, A. Monacchi, T. Khatib, and W. Elmenreich.
Integration of legacy appliances into home energy
ma...
Possible components of an (H)EMS
• Smart Meter
– Measures overall energy consumption in real time
• Smart Appliances
– Is ...
HEMS architecture
5
Wilfried Elmenreich
Smart Appliances
• A smart appliance consists of
– a communication interface
– a local processing and decision unit
– the ...
Data Management
• Modeling
– Building information
– Building automation and description
– User information and preferences...
Appliance and Description Model
Integration of Legacy Devices
• From data management view
– Device stub provides a unique
mapping of all devices
• How to ...
Power consumption as information
• A power draw is an information, e.g. on/off
– Aggregated power draw measured at smart m...
Non-Intrusive Load Monitoring
12
Wilfried Elmenreich
P
t
P
t
P
t
P
t
P
t
P
t
Find out which com-
bination of power pro-
fi...
• Knapsack problem base approach
• NP-hard problem
• Used metaheuristic algorithms
• Evolutionary algorithm
• Differential...
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
t-...
• Using a dataset of power draws from measurement campaign
• Dataset GREEND
• Households in Austria, Italy
• 1s measuremen...
Example of power readings from GREEND
16
Wilfried Elmenreich
• Load disaggregation
• Ground truth vs. estimated
Results – Load identification
• Grouped appliances
Results – Multiple metering points
Results – Device Profile
Results – Load Identification Model
Summary
• The smart home energy management systems needs
– Smart appliances
– Automated device integration
– Support for l...
22
Thank you very much for your attention!
Questions and
comments are welcome!
D. Egarter, A. Monacchi, T. Khatib, and W. ...
Further References
• Andrea Monacchi, Dominik Egarter, Wilfried Elmenreich,
Salvatore D'Alessandro, Andrea M. Tonello. GRE...
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Elmenreich Interoperability between smart and legacy devices in energy management systems

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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.

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Elmenreich Interoperability between smart and legacy devices in energy management systems

  1. 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. 2. Overview • Home Energy Management System Architecture • Smart Devices • Non-intrusive load monitoring for legacy device integration • Modeling device profiles and load models
  3. 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. 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. 5. HEMS architecture 5 Wilfried Elmenreich
  6. 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. 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. 8. Appliance and Description Model
  9. 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. 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. 11. Non-Intrusive Load Monitoring 12 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 P P Measure the overall power consumption over time1. 2.
  12. 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. 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 t-1 x2 t x3 t x2 t+1 x3 t+1 P t Appliance model Fractional hidden markov model – Household model Aggregated power load
  14. 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. 15. Example of power readings from GREEND 16 Wilfried Elmenreich
  16. 16. • Load disaggregation • Ground truth vs. estimated Results – Load identification
  17. 17. • Grouped appliances Results – Multiple metering points
  18. 18. Results – Device Profile
  19. 19. Results – Load Identification Model
  20. 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) 21 Wilfried Elmenreich
  21. 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. 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. 23 Wilfried Elmenreich http://www.monergy-project.eu/

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