Abstract
As renewable energy resources become a significant source of electricity production, the stable operation of the electrical grid becomes increasingly difficult. Demand-side control of the electrical grid load solves this problem and enables better utilization of renewable energy such as wind or solar power. Adjusting the grid load to meet the renewable production levels requires knowledge about the composition of the grid load as well as the ability to schedule individual loads. We propose a Smart Plug solution capable of accurately classifying the connected electrical load as well as running the Neural Network-based classification on the Smart Plug. The Smart Plug is WiFi-capable allowing wireless measurements as well as remote control of the connected electrical load. We took measurements with the Smart Plug prototype of common household electrical loads and achieved very high accuracy. This accuracy rate can be achieved with on-device measurement and on-device NN inference in less than 2.5 s. Multiple NN-based classification methods and measurements of different amounts of data were examined (measurement profiles).
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
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Chang, X., et al.: From insight to impact: building a sustainable edge computing platform for smart homes. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), pp. 928–936 (2018). https://doi.org/10.1109/PADSW.2018.8644647
da S. Veloso, A.F., de Oliveira, R.G., Rodrigues, A.A., Rabelo, R.A.L., Rodrigues, J.J.P.C.: Cognitive smart plugs for signature identification of residential home appliance load using machine learning: from theory to practice. In: 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6 (2019)
David, R., et al.: Tensorflow lite micro: embedded machine learning for tinyml systems. In: Smola, A., Dimakis, A., Stoica, I. (eds.) Proceedings of Machine Learning and Systems, vol. 3, pp. 800–811 (2021). https://proceedings.mlsys.org/paper/2021/file/d2ddea18f00665ce8623e36bd4e3c7c5-Paper.pdf
Dutta, D.L., Bharali, S.: Tinyml meets IoT: a comprehensive survey. Internet Things 16, 100461 (2021)
Espressif Systems: Esp32. https://www.espressif.com/en/products/socs/esp32
Espressif Systems: Optimised neural network functions for espressif chipsets. https://github.com/espressif/esp-nn
Gomes, L., Sousa, F., Vale, Z.: An intelligent smart plug with shared knowledge capabilities. Sensors 18(11), 3961 (2018). https://doi.org/10.3390/s18113961
Jaradat, M., Jarrah, M., Jararweh, Y., Al-Ayyoub, M., Bousselham, A.: Integration of renewable energy in demand-side management for home appliances. In: 2014 International Renewable and Sustainable Energy Conference (IRSEC), pp. 571–576 (2014)
Mtshali, P., Khubisa, F.: A smart home appliance control system for physically disabled people. In: 2019 Conference on Information Communications Technology and Society (ICTAS), pp. 1–5 (2019)
Murshed, M.G.S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., Hussain, F.: Machine learning at the network edge: a survey. ACM Comput. Surv. 54(8) (2021). https://doi.org/10.1145/3469029
Németh., D., Tornai., K.: SP4LC: a method for recognizing power consumers in a smart plug. In: Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS, pp. 69–77. INSTICC, SciTePress (2022). https://doi.org/10.5220/0010982800003203
Petrović, T., Morikawa, H.: Active sensing approach to electrical load classification by smart plug. In: 2017 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2017)
Raspberry Pi Foundation: RP2040. https://www.raspberrypi.com/products/rp2040/
Ridi, A., Gisler, C., Hennebert, J.: A survey on intrusive load monitoring for appliance recognition. In: 2014 22nd International Conference on Pattern Recognition, pp. 3702–3707 (2014)
Acknowledgement
This work has been supported by the Fund FK 137 608 of the Hungarian National Research, Development and Innovation Office.
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
Németh, D.I., Tornai, K. (2023). Fast Electrical Load Classification Using a Dimmer-Based Smart Plug. In: Klein, C., Jarke, M., Ploeg, J., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. SMARTGREENS VEHITS 2022 2022. Communications in Computer and Information Science, vol 1843. Springer, Cham. https://doi.org/10.1007/978-3-031-37470-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-37470-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37469-2
Online ISBN: 978-3-031-37470-8
eBook Packages: Computer ScienceComputer Science (R0)