Advertisement

ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms

Conference paper
  • 127 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1199)

Abstract

The research field of energy analytics is concerned with the collection and processing of data related to electrical power generation and consumption. Electricity consumption data can reveal information pertaining to the nature of underlying appliances, their mode of operation, and many other aspects. Sudden load changes, so-called events, constitute the principal source of information in such time series data, thus their reliable detection and interpretation is a prerequisite for accurate energy analytics. The development of event detection algorithms is, however, hampered due to the unavailability of comprehensive data sets that feature energy consumption time series with corresponding event annotations. We hence present ANNO, a tool to provide annotations to time series consumption data in a supervised fashion and use them for the development of energy analytics algorithms, in this work.

Keywords

Load signature analysis Supervised data set annotation 

References

  1. 1.
    Anderson, K., Filip, A., Benítez, D., Carlson, D., Rowe, A., Bergés, M.: BLUED: a fully labeled public dataset for event-based nonintrusive load monitoring research. In: Proceedings of the 2nd Workshop on Data Mining Applications in Sustainability (SustKDD) (2011)Google Scholar
  2. 2.
    Armel, K.C., Gupta, A., Shrimali, G., Albert, A.: Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52(1), 213–234 (2013)CrossRefGoogle Scholar
  3. 3.
    Barker, S., Kalra, S., Irwin, D., Shenoy, P.: Empirical characterization and modeling of electrical loads in smart homes, pp. 1–10 (2013)Google Scholar
  4. 4.
    Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., Albrecht, J.: Smart*: an open data set and tools for enabling research in sustainable homes. In: Proceedings of the Workshop on Data Mining Applications in Sustainability (SustKDD) (2012)Google Scholar
  5. 5.
    Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: insights into home energy consumption in India. In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (BuildSys) (2013)Google Scholar
  6. 6.
    Bonaldi, E., de Lacerda de Oliveira, L., Borges da Silva, J., Lambert-Torres, G., Borges da Silva, L.: Predictive maintenance by electrical signature analysis to induction motors. In: Esteves Araújo, R. (ed.) Induction Motors - Modelling and Control. IntechOpen (2012)Google Scholar
  7. 7.
    Buneeva, N., Reinhardt, A.: AMBAL: realistic load signature generation for load disaggregation performance evaluation. In: 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 443–448 (2017)Google Scholar
  8. 8.
    Chen, D., Irwin, D.E., Shenoy, P.J.: SmartSim: a device-accurate smart home simulator for energy analytics. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 686–692 (2016)Google Scholar
  9. 9.
    Ehrhardt-Martinez, K., Donnelly, K., Laitner, J.: Advanced Metering Initiatives and Residential Feedback Programs: a Meta-Review for Household Electricity-Saving Opportunities. American Council for an Energy-Efficient Economy (2010)Google Scholar
  10. 10.
    Gao, J., Giri, S., Kara, E.C., Bergés, M.: PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys) (2014)Google Scholar
  11. 11.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRefGoogle Scholar
  12. 12.
    Heartex Inc.: A Curated List of Awesome Data Labeling Tools. https://github.com/heartexlabs/awesome-data-labeling
  13. 13.
    Kelly, J., Knottenbelt, W.: The UK-DALE dataset: domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2(150007) (2015). http://jack-kelly.com/data/
  14. 14.
    Klemenjak, C., Reinhardt, A., Pereira, L., Berges, M., Makonin, S., Elmenreich, W.: Electricity consumption data sets: pitfalls and opportunities. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), pp. 159–162 (2019)Google Scholar
  15. 15.
    Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Proceedings of the Workshop on Data Mining Applications in Sustainability (SustKDD) (2011)Google Scholar
  16. 16.
    Kriechbaumer, T., Jacobsen, H.A.: BLOND, a building-level office environment dataset of typical electrical appliances. Sci. Data 5, 180048 (2018)CrossRefGoogle Scholar
  17. 17.
    Masoodian, M., André, E., Kugler, M., Reinhart, F., Rogers, B., Schlieper, K.: USEM: a ubiquitous smart energy management system for residential homes. Int. J. Adv. Intell. Syst. 7(3&4), 519–532 (2014)Google Scholar
  18. 18.
    Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M.: GREEND: an energy consumption dataset of households in Italy and Austria. In: Proceedings of the 5th IEEE International Conference on Smart Grid Communications (SmartGridComm) (2014)Google Scholar
  19. 19.
    Pereira, L., Ribeiro, M., Nunes, N.: Engineering and deploying a hardware and software platform to collect and label non-intrusive load monitoring datasets. In: Proceedings of the 5th IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT), pp. 1–9 (2017)Google Scholar
  20. 20.
    Picon, T., Nait Meziane, M., Ravier, P., Lamarque, G., Novello, C., Le Bunetel, J.C., Raingeaud, Y.: COOLL: Controlled On/Off Loads Library, a Public Dataset of High-Sampled Electrical Signals for Appliance Identification. arXiv preprint arXiv:1611.05803 [cs.OH] (2016)
  21. 21.
    Reinhardt, A., et al.: 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), pp. 1–9 (2012)Google Scholar
  22. 22.
    Sadeghianpourhamami, N., Ruyssinck, J., Deschrijver, D., Dhaene, T., Develder, C.: Comprehensive feature selection for appliance classification in NILM. Energy Build. 151, 98–106 (2017)CrossRefGoogle Scholar
  23. 23.
    Sandlin, H.A., Kurniawan Wijaya, T., Aberer, K., Nunes, N.: A collaborative framework for annotating energy datasets. In: Proceedings of the 2015 Workshop for Sustainable Development at the 2015 IEEE International Conference on Big Data (BigData) (2015)Google Scholar
  24. 24.
    Weiss, M., Helfenstein, A., Mattern, F., Staake, T.: Leveraging smart meter data to recognize home appliances. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom) (2012)Google Scholar
  25. 25.
    Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. MDPI Sens. 12, 16838–16866 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of InformaticsTechnische Universität ClausthalClausthal-ZellerfeldGermany

Personalised recommendations