Abstract
With the rising implementation of Home Energy Management Systems (HEMS), active studies had been done relative to power monitoring alternatives. Load monitoring is an essential block of HEMS; therefore, the improvement of simplicity and convenience in load monitoring is crucial for the HEMS market expansion. This paper proposes the use of Artificial Neural Network models for the classification of common electrical appliances based on the extracted distinctive current starting transient features of electrical appliances. This research’s main challenges are: conducting reliable instrumentation practice with an appropriate choice of instruments, extracting distinctive features in the current transient, and analyzing the ANN classifier for good performance using artificial intelligence methods. The analysis would compare the performance of time-domain inputs and frequency-domain inputs to the ANN classifier. By selecting appropriate frequency-domain features as input to the ANN classifier, it was shown that up to 86% classification accuracy could be obtained using the proposed method, justifying our hypothesis that multiple non-intrusive load monitoring using a single sensor is indeed plausible.
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
Aliero MS, Qureshi KN, Pasha MF, Jeon G (2021) Smart home energy management systems in internet of things networks for green cities demands and services. Environ Technol Innov 22:101443. https://doi.org/10.1016/j.eti.2021.101443
Energy Efficiency in Malaysia Sustainable Production and Consumption: Phasing-out Malaysia’s Non-Energy Efficient Products (2012). Association of Water & Energy Research Malaysia, pp 3–4. http://www.awer.org.my/publication/report/Energy_Efficiency_In_Malaysia.pdf. Accessed 6 Sept 2021
Smart home of the future: Beyond borders from MIMOS. Business Circle. http://www.businesscircle.com.my/smart-home-of-the-future-beyond-borders-from-mimos. Accessed 10 Oct 2015
Chen YT (2017) The factors affecting electricity consumption and the consumption characteristics in the residential sector—a case example of Taiwan. Sustainability 9:1484. https://doi.org/10.3390/su9081484
Total electrical final consumption statistics. In: Hub, Malaysia Energy Inf. http://meih.st.gov.my. Accessed 10 Oct 2020
Zhuang M, Shahidehpour M, Li Z (2018) An overview of non-intrusive load monitoring: approaches, business applications, and challenges. In: 2018 international conference on power system technology (POWERCON). IEEE, pp 4291–4299
Lukaszewski R, Liszewski K, Winiecki W (2013) Methods of electrical appliances identification in systems monitoring electrical energy consumption. In: 2013 IEEE 7th international conference on intelligent data acquisition and advanced computing systems (IDAACS). IEEE, pp 10–14
Reinhardt A, Burkhardt D, Zaheer M, Steinmetz R (2012) Electric appliance classification based on distributed high resolution current sensing. In: 37th annual IEEE conference on local computer networks -workshops. IEEE, pp 999–1005
Kim Y, Kong S, Ko R, Joo SK (2014) Electrical event identification technique for monitoring home appliance load using load signatures. In: 2014 IEEE international conference on consumer electronics (ICCE). IEEE, pp 296–297
Chang HH, Wiratha PW, Chen N (2014) A Non-intrusive load monitoring system using an embedded system for applications to unbalanced residential distribution systems. Energy Procedia 61:146–150. https://doi.org/10.1016/j.egypro.2014.11.926
Khan MMR, Siddique MAB, Sakib S (2019) Non-intrusive electrical appliances monitoring and classification using K-nearest neighbors. In: 2019 2nd international conference on innovation in engineering and technology (ICIET). IEEE, pp. 1–5
Reddy R, Garg V, Pudi V (2020) A feature fusion technique for improved non-intrusive load monitoring. Energy Inform 3:9. https://doi.org/10.1186/s42162-020-00112-w
Srinivasarengan K, Goutam YG, Chandra MG, Kadhe S (2013) A framework for non intrusive load monitoring using bayesian inference. In: 2013 seventh international conference on innovative mobile and internet services in ubiquitous computing. IEEE, pp 427–432
Chang HH (2012) Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies 5:4569–4589. https://doi.org/10.3390/en5114569
National Instruments (2017). Understanding FFTs and Windowing. National Instruments. http://www.ni.com/white-paper/4844/en. Accessed 10 May 2020
Zuffereym D, Gisler C, Khaled OA, Hennebert J (2012) Machine learning approaches for electric appliance classification. IEEE Explore. 978-1-4673-0382-8/12/
Acknowledgements
The authors would like to thank Centre of Robotics, Instrumentation and Automation (CeRIA), Universiti Teknikal Malaysia Melaka (UTeM) for the facilities and financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Izzuddin, T.A., Safri, N.M., Munn, O.S., Sani, Z.M., Nasir, M.N.M. (2022). Classification of Domestic Electrical Appliances Based on Starting Transient Using Artificial Intelligence Methods. In: Md. Zain, Z., Sulaiman, M.H., Mohamed, A.I., Bakar, M.S., Ramli, M.S. (eds) Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol 842. Springer, Singapore. https://doi.org/10.1007/978-981-16-8690-0_41
Download citation
DOI: https://doi.org/10.1007/978-981-16-8690-0_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8689-4
Online ISBN: 978-981-16-8690-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)