Skip to main content

Advertisement

SpringerLink
Log in

Integration of legacy appliances into home energy management systems

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://protege.stanford.edu

  2. http://www.monergy-project.eu/appliance-ontology/

References

  • Acampora G, Loia V, Vitiello A (2013) Distributing fuzzy reasoning through fuzzy markup language: an application to ambient intelligence. In: On the power of fuzzy markup language. Springer, Berlin, pp 33–50

  • Armel KC, Gupta A, Shrimali G, Albert A (2013) Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52:213–234

    Article  Google Scholar 

  • Barker S, Mishra A, Irwin D, Cecchet E, Shenoy P, Albrecht J (2012) Smart*: an open data set and tools for enabling research in sustainable homes. In: Proceedings of KDD workshop on data mining applications in sustainability (SustKDD)

  • Bergman D, Jin D, Juen J, Tanaka N, Gunter C, Wright A (2011) Nonintrusive load-shed verification. Pervasive Comput IEEE 10(1):49–57

    Article  Google Scholar 

  • Bonino D, Corno F (2008) Dogont—ontology modeling for intelligent domotic environments. The semantic web—ISWC 2008, vol 5318. Lecture notes in computer science. Springer, Berlin, pp 790–803

  • Carlson DR, Matthews HS, Berges M (2013) One size does not fit all: averaged data on household electricity is inadequate for residential energy policy and decisions. Energy Build 64:132–144

    Article  Google Scholar 

  • Compton M, Barnaghi P, Bermudez L, Garca-Castro R, Corcho O, Cox S, Graybeal J, Hauswirth M, Henson C, Herzog A, Huang V, Janowicz K, Kelsey WD, Phuoc DL, Lefort L, Leggieri M, Neuhaus H, Nikolov A, Page K, Passant A, Sheth A, Taylor K (2012) The SSN ontology of the w3c semantic sensor network incubator group. Web semantics: science, services and agents on the world wide web 17

  • Dargie W (2009) Context-aware computing and self-managing systems, 1st edn. Chapman & Hall/CRC, Boca Raton

    Book  Google Scholar 

  • Dong M, Meira PC, Xu W, Chung CY (2013) Non-intrusive signature extraction for major residential loads. IEEE Trans Smart Grid 4(3):1421–1430

    Article  Google Scholar 

  • Egarter D, Sobe A, Elmenreich W (2013) Evolving non-intrusive load monitoring. In: Proceedings of the applications of evolutionary computation conference, pp 182–191

  • Egarter D, Prokop C, Elmenreich W (2014) Load hiding of household’s power demand. In: IEEE international conference on smart grid communications (SmartGridComm), IEEE, pp 854–859

  • Egarter D, Bhuvana VP, Elmenreich W (2015) PALDi: online load disaggregation via particle filtering. IEEE Trans Instrum Meas 64:467–477

    Article  Google Scholar 

  • Elmenreich W, Egarter D (2012) Design guidelines for smart appliances. In: Proceedings of the 10th international workshop on intelligent solutions in embedded systems, pp 76–82

  • Englert F, Schmitt T, Kössler S, Reinhardt A, Steinmetz R (2013) How to autoconfigure your smart home? High resolution power measurements to the rescue. In: Proceeding of the 4th international conference on future energy systems

  • Goncalves H, Ocneanu A, Berges M (2011) Unsupervised disaggregation of appliances using aggregated consumption data. In: Proceedings of KDD workshop on data mining applications in sustainability (SustKDD)

  • Graditi G, Ippolito M, Lamedica R, Piccolo A, Ruvio A, Santini E, Siano P, Zizzo G (2015) Innovative control logics for a rational utilization of electric loads and air-conditioning systems in a residential building. Energy Build 102:1–17

    Article  Google Scholar 

  • Greveler U, Justus B, Loehr D (2012) Multimedia content identification through smart meter power usage profiles. In: Proceedings of the international conference on information and knowledge engineering IKE’12

  • Hart G (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891

    Article  Google Scholar 

  • Heath T, Bizer C (2011) Linked data: evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, vol 1, pp 1–136

  • Jammes F, Smit H (2005) Service-oriented paradigms in industrial automation. IEEE Trans Ind Inform 1(1):62–70

    Article  Google Scholar 

  • Kim H, Marwah M, Arlitt MF, Lyon G, Han J (2011) Unsupervised disaggregation of low frequency power measurements. In: Proceedings of the 11th SIAM international conference on data mining

  • Kofler MJ, Kastner W (2013) Towards an ontology representing building physics parameters for increased energy efficiency in smart home operation. In: Proceedings of the 2nd central European symposium on building physic (CESBP2013), Vienna, Austria

  • Kofler MJ, Reinisch C, Kastner W (2012) An ontological weather representation for improving energy-efficiency in interconnected smart home systems. In: Proceedings of applied simulation and modelling/artificial intelligence and soft computing (ASC2012), Napoli, Italy

  • Kofler MJ, Vázquez FI, Kastner W (2013) An Ontology for representation of user habits and building context in future smart homes. In: Proceedings of the 20th workshop on intelligent computing in engineering (EG-ICE2013), Vienna, Austria

  • Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. In: Proceeding of the SustKDD workshop on data mining applications in sustainability

  • Kolter Z, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. In: Proceedings of the international conference on artifical intelligence and statistics

  • Liang J, Ng S, Kendall G, Cheng J (2010) Load signature study, part i: basic concept, structure, and methodology. IEEE Trans Power Deliv 25(2):551–560

    Article  Google Scholar 

  • Lin G, Lee S, Hsu JJ, Jih W (2010) Applying power meters for appliance recognition on the electric panel. In: Proceedings of IEEE conference on industrial electronics and applications (ICIEA)

  • Lisovich M, Mulligan D, Wicker S (2010) Inferring personal information from demand-response systems. IEEE Secur Priv 8(1):11–20

    Article  Google Scholar 

  • Makonin S, Popowich F, Bartram L, Gill B, Bajic IV (2013a) AMPds: a public dataset for load disaggregation and eco-feedback research. In: Proceeding of the IEEE conference on electrical power and energy (EPEC)

  • Makonin S, Popowich F, Gill B (2013b) The cognitive power meter: looking beyond the smart meter. In: Proceedings of the 26th IEEE Canadian conference on electrical and computer engineering CCECE

  • Molina-Markham A, Shenoy P, Fu K, Cecchet E, Irwin D (2010) Private memoirs of a smart meter. In: Proceedings of 2nd ACM workshop on embedded sensing systems for energy-efficiency in building

  • Monacchi A, Egarter D, Elmenreich W (2013a) Integrating households into the smart grid. In: Proceedings of the IEEE workshop on modeling and simulation of cyber-physical energy systems

  • Monacchi A, Elmenreich W, D’Alessandro S, Tonello AM (2013b) Strategies for energy conservation in Carinthia and Friuli-Venezia Giulia. In: Proceedings of the 39th annual conference of the IEEE industrial electronics society

  • Monacchi A, Egarter D, Elmenreich W, D’Alessandro S, Tonello AM (2014) GREEND: an energy consumption dataset of households in Italy and Austria. In: IEEE international conference on smart grid communications (SmartGridComm), IEEE, pp 511–516

  • Nguyen TA, Aiello M (2013) Energy intelligent buildings based on user activity: a survey. Energy Build 56:244–257

    Article  Google Scholar 

  • Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform 7(3):381–388

    Article  Google Scholar 

  • Parson O, Ghosh S, Weal M, Rogers A (2012) Non-intrusive load monitoring using prior models of general appliance types. In: Proceedings of the twenty-sixth conference on artificial intelligence (AAAI-12)

  • Pfisterer D, Romer K, Bimschas D, Kleine O, Mietz R, Truong C, Hasemann H, Kro andller A, Pagel M, Hauswirth M, Karnstedt M, Leggieri M, Passant A, Richardson R,(2011) Spitfire: toward a semantic web of things. IEEE Commun Mag 49(11):40–48. doi:10.1109/MCOM.2011.6069708

  • Pitzek S, Elmenreich W (2005) Plug-and-play: bridging the semantic gap between application and transducers. In: Proceedings of the 10th IEEE international conference on emerging technologies and factory automation

  • Preuveneers D, Bergh JVD, Wagelaar D, Georges A, Rigole P, Clerckx T, Berbers E, Coninx K, Bosschere KD (2004) Towards an extensible context ontology for ambient intelligence. In: Proceedings of the second European symposium on ambient intelligence

  • Raskin RG (2004) Enabling semantic interoperability for earth science data. http://sweet.jpl.nasa.gov

  • Reinhardt A, Baumann P, Burgstahler D, Hollick M, Chonov H, Werner M, Steinmetz R (2012) On the accuracy of appliance identification based on distributed load metering data. In: Proceedings of the 2nd IFIP conference on sustainable internet and ICT for sustainability (SustainIT)

  • Reinisch C, Kofler MJ, Iglesias F, Kastner W (2011) Thinkhome energy efficiency in future smart homes. EURASIP J Embed Syst 2011:1–1:18. doi:10.1155/2011/104617

  • Siano P, Graditi G, Atrigna M, Piccolo A (2013) Designing and testing decision support and energy management systems for smart homes. J Ambient Intel Human Comput 4(6):651–661

    Article  Google Scholar 

  • Skopik F (2012) Security is not enough! On privacy challenges in smart grids. Int J Smart Grid Clean Energy 1(1):7–14

    Article  Google Scholar 

  • Srinivasan D, Ng WS, Liew A (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21(1):398–405

    Article  Google Scholar 

  • Stavropoulos TG, Vrakas D, Vlachava D, Bassiliades N (2012) Bonsai: a smart building ontology for ambient intelligence. In: Proceedings of the 2nd international conference on web intelligence, mining and semantics

  • Suzuki K, Inagaki S, Suzuki T, Nakamura H, Ito K (2008) Nonintrusive appliance load monitoring based on integer programming. In: Proceedings of international conference on instrumentation, control, information technology and system integration (SICE)

  • Tomic S, Fensel A, Pellegrini T (2010) Sesame demonstrator: ontologies, services and policies for energy efficiency. In: Proceedings of the 6th international conference on semantic systems (I-SEMANTICS)

  • Villaverde B, Alberola R, Jara A, Fedor S, Das S, Pesch D (2013) Service discovery protocols for constrained machine-to-machine communications. Commun Surv Tutor IEEE PP (99):1–20. doi:10.1109/SURV.2013.102213.00229

  • Wong YF, Drummond T (2014) Real-time load disaggregation algorithm using particle-based distribution truncation with state occupancy model. Electron Lett 50:697–699(2)

  • Zaidi AA, Kupzog F, Zia T, Palensky P (2010) Load recognition for automated demand response in microgrids. In: Proceedings of the 36th IEEE conference on industrial electronics (IECON)

  • Zeifman M (2012) Disaggregation of home energy display data using probabilistic approach. IEEE Trans Consum Electron 58(1):23–31

    Article  MathSciNet  Google Scholar 

  • Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84

    Article  Google Scholar 

  • Zoha A, Gluhak A, Nati M, Imran M (2013) Low-power appliance monitoring using factorial hidden markov models. In: Proceedings of IEEE eighth international conference on intelligent sensors, sensor networks and information processing

Download references

Author information

Authors and Affiliations

  1. Institute of Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt, Lakeside Park B10.2, 9020, Klagenfurt am Wörthersee, Austria

    Dominik Egarter, Andrea Monacchi & Wilfried Elmenreich

  2. Department of Energy Engineering and Built Environment, An-Najah National University, 97300, Nablus, Palestine

    Tamer Khatib

Authors
  1. Dominik Egarter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Monacchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tamer Khatib
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wilfried Elmenreich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dominik Egarter.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Egarter, D., Monacchi, A., Khatib, T. et al. Integration of legacy appliances into home energy management systems. J Ambient Intell Human Comput 7, 171–185 (2016). https://doi.org/10.1007/s12652-015-0312-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-015-0312-9

Keywords

Search

Navigation