Abstract
With the introduction of the smart grid, smart meters and smart plugs, it is possible to know the energy consumption of a smart home, either per appliance or aggregate. Some recent works have used energy consumption traces to detect anomalies, either in the behavior of the inhabitants or in the operation of some device in the smart home. To train and test the algorithms that detect these anomalies, it is necessary to have extensive and well-annotated consumption traces. However, this type of traces is difficult to obtain. In this paper we describe a highly configurable synthetic electrical trace generator, with characteristics similar to real traces, that can be used in this type of study. In order to have a more realistic behavior, the traces are generated by adding the consumption of several simulated appliances, which precisely represent the consumption of different typical electrical devices. Following the behavior of the real traces, variations at different scales of time and anomalies are introduced to the aggregated smart home energy consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The open-access REFIT Electrical Load Measurements dataset can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4.
References
Barker, S., Kalra, S., Irwin, D., Shenoy, P.: Empirical characterization and modeling of electrical loads in smart homes. In: 2013 International Green Computing Conference Proceedings pp. 1–10 (2013). https://doi.org/10.1109/IGCC.2013.6604512
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. SustKDD August 111(112), 108 (2012)
Beckel, C., Kleiminger, W., Cicchetti, R., Staake, T., Santini, S.: The eco data set and the performance of non-intrusive load monitoring algorithms. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, pp. 80–89. ACM (2014)
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). https://doi.org/10.1109/SmartGridComm.2017.8340657
Cabantac, S.E.P., Garcia, F.C.C., Macabebe, E.Q.B.: A single point sensing approach for residential power monitoring with appliance recognition using random forest. In: TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 1154–1157 (2018). https://doi.org/10.1109/TENCON.2018.8650282
Chen, D., Irwin, D., Shenoy, P.: Smartsim: A device-accurate smart home simulator for energy analytics. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 686–692 (2016). https://doi.org/10.1109/SmartGridComm.2016.7778841
Firth, S., Cole, G.J., Kane, T., Fouchal, F., Hassan, T.M.: An open science approach for building performance studies (2018)
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)
Klemenjak, C., Kovatsch, C., Herold, M., Elmenreich, W.: A synthetic energy dataset for non-intrusive load monitoring in households. Sci. Data 7(1), 108 (2020)
Kolter, J.Z., Johnson, M.J.: Redd: A public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA. vol. 25, pp. 59–62 (2011)
Kumar, A., Bhattacharjee, P.: Non-intrusive appliance identification for energy disaggregation of indian households-an use case for energy informatics. In: 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), pp. 239–242 (2018). https://doi.org/10.1109/iSES.2018.00059
Liao, J., Stankovic, L., Stankovic, V.: Detecting household activity patterns from smart meter data. In: 2014 International Conference on Intelligent Environments. pp. 71–78 (2014). https://doi.org/10.1109/IE.2014.18
Makonin, S., Ellert, B., Bajić, I.V., Popowich, F.: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci. Data 3, 160037 (2016)
Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M.: Greend: an energy consumption dataset of households in Italy and Austria. In: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 511–516. IEEE (2014)
Moure-Garrido, M., Campo, C., Garcia-Rubio, C.: Entropy-based anomaly detection in household electricity consumption. Energies 15(5) (2022). https://doi.org/10.3390/en15051837, https://www.mdpi.com/1996-1073/15/5/1837
Murray, D., Liao, J., Stankovic, L., Stankovic, V.: Understanding usage patterns of electric kettle and energy saving potential. Appl. Energy 171, 231–242 (2016). https://doi.org/10.1016/j.apenergy.2016.03.038
Murray, D., Stankovic, L., Stankovic, V.: An electrical load measurements dataset of united kingdom households from a two-year longitudinal study. Sci. Data 4(1), 160122 (2017). https://doi.org/10.1038/sdata.2016.122
Reinhardt, A., et al.: On the accuracy of appliance identification based on distributed load metering data. In: 2012 Sustainable Internet and ICT for Sustainability (SustainIT), pp. 1–9. IEEE (2012)
Revuelta Herrero, J., et al.: Non intrusive load monitoring (nilm): a state of the art. In: De la Prieta, F., et al. (eds.) Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017, pp. 125–138. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_12
Acknowledgements
This work was supported by the Spanish Government under the research project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020-113795RB-C32, funded by MCIN/AEI/10.13039/501100011033 and the project MAGOS TEC2017-84197-C4-1-R, and by the Comunidad de Madrid (Spain) under the projects: CYNAMON (P2018/TCS-4566), co-financed by European Structural Funds (ESF and FEDER), and the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Garcia-Rubio, C., Campo, C., Moure-Garrido, M. (2023). Synthetic Generation of Electrical Consumption Traces in Smart Homes. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_68
Download citation
DOI: https://doi.org/10.1007/978-3-031-21333-5_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21332-8
Online ISBN: 978-3-031-21333-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)