Interoperability Between Smartand Legacy Devices in Energy Management Systems

Networked and Embedded Systems

Wilfried Elmenreich | 2015-09-28Workshop Energieinformatik45. GI-Jahrestagung "Informatik, Energie und Umwelt"

Overview

• Home Energy Management System Architecture

• Smart Devices• Non-intrusive load monitoring for legacy

device integration• Modeling device profiles and load models

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

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)

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HEMS architecture

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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

Wilfried Elmenreich

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

Appliance and Description Model

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?

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

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Non-Intrusive Load Monitoring

Wilfried Elmenreich

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Find out which com-bination of power pro-files match measuredpower consumption.

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Measure the overall power consumption

over time

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• 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

Particle Filter Based Load DisaggregationPALDI

off

on

100W

0W

off

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300W

0W

5Woff

on1

on2

0W

1000W

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yt-1 yt yt+1

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Appliance model Fractional hidden markov model – Household model

Aggregated power load

• 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

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Example of power readings from GREEND

Wilfried Elmenreich

• Load disaggregation• Ground truth vs. estimated

Results – Load identification

• Grouped appliances

Results – Multiple metering points

Results – Device Profile

Results – Load Identification Model

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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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Thank you very much for your attention!

Questions andcomments 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

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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.

Wilfried Elmenreich

http://www.monergy-project.eu/