A comparative study on appliance recognition with power parameters by using machine learning algorithms

Year 2021, , 292 - 300, 15.08.2021

Yılmaz Güven

https://doi.org/10.35860/iarej.873644

Abstract

Recently, machine Learning algorithms are widely used in many fields. Especially, they are really good to create prediction models for problems which are not easy to solve with conventional programming techniques. Although, there are many different kinds of machine learning algorithms, results of applications are varying depend on type of data and correlation of information. In this study, different machine learning algorithms have been used for appliance recognition. The measurement data of Appliance Consumption Signatures database and some derivative values have been used for training and testing. Additionally, a data pre-processing technique and its effects on results have been presented. Filtering corrupted data and removing uncertain measurement value has improved the quality of machine learning. Combination of machine learning algorithms is best way to work with uncertain values. Different feature extraction methods and data pre-processing techniques are crucial in machine learning. Therefore, this study aims to develop a high accurate appliance recognition technique by combining grey relational analysis and an ensemble classification method. The results of this new method have been presented comparatively to show the improvement for itself and previous studies that uses the same database.

Keywords

Appliance Recognition, Data Pre-processing, Feature Extraction, Grey Relational Analysis, Machine Learning

References

• 1. Zhaou, H. X., Magoules F., A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 2012. 16: p.3586-3592.
• 2. Lin, Y. H., Tsai, M. S., An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring with Automated Multiobjective Power Scheduling. IEEE Transactions on Smart Grid, 2015. 6: p.1839-1851.
• 3. Sanchez-Sutil, F., Cano-Ortega, A., Hernandez, J.C., Rus-Casas, C., Development and Calibration of an Open Source, Low-Cost Power Smart Meter Prototype for PV Household-Prosumers, MDPI Electronics, 2019. 8: p.878.
• 4. Medico, R., De Baets, L., Gao, J. et al., A voltage and current measurement dataset for plug load appliance identification in households, Nature Scientific Data, 2020. 7: p.49.
• 5. Ridi, A., Gisler, C., Hennebert, J., ACS-F2 A new database of appliance consumption signatures. 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2017. 6: p.145-150.
• 6. Ruzzelli, A. G., Nicolas, C., Schoofs, A., O'Hare, G. M. P., Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2017. 7: p.1-9.
• 7. Huang, A. Q., Crow, M. L., Heydt, G. T., Zheng, J. P., Dale, S. J., The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet, Proceedings of the IEEE, 2011. 99(1): p.133-148.
• 8. Mpawenimana, I., Pegatoquet, A., Soe, W. T., Belleudy, C., Appliances Identification for Different Electrical Signatures using Moving Average as Data Preparation. Ninth International Green and Sustainable Computing Conference (IGSC), 2018. 9: p.1-6.
• 9. Qaisar, S. M., Alsharif, F., An Adaptive Rate Time-Domain Approach for a Proficient and Automatic Household Appliances Identification. International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019, p.1-4.
• 10. Hamid, O., Barbarosou, M., Papageorgas, P., Prekas, K., Salame, C-T., Automatic recognition of electric loads analysing the characteristic parameters of the consumed electric power through a Non-Intrusive Monitoring methodology. Energy Procedia, 2017. 119: p.742-751.
• 11. Khawaja, A.S., Anpalagan, A., Naeem, M., Venkatesh, B. Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting, Electric Power Systems Research, 2020. 179: p.1-7.
• 12. Himeur, Y., Alsalemi, A., Bensaali, F., Amira A., Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree, Applied Energy, 2020. 267: p.1-16.
• 13. Huchtkoetter, J., Tepe, M.A., Reinhardt, A. The Impact of Ambient Sensing on the Recognition of Electrical Appliances. Energies 2021. 14: p.188.
• 14. Mihailescu, R-C., Hurtig, D., Olsson, C., End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning, Internet of Things, 2020. 11: p.1-10.
• 15. Shin, E., Khamesi, A. R., Bahr, Z., Silvestri, S. and Baker, D. A., A User-Centered Active Learning Approach for Appliance Recognition, IEEE International Conference on Smart Computing (SMARTCOMP), 2020. p. 208-213.
• 16. Institute of Complex Systems [ Cites 2020 11 June]; Available from: https://icosys.ch/acs-f2.
• 17. Zhang, S., Zhang, C., Yang, Q., Data preparation for data mining. Applied Artificial Intelligence, 2003 17(5-6): p.375-381.
• 18. Sallehuddin, R. Shamsuddin, S. M. H., Hashim, S. Z. M., Application of Grey Relational Analysis for Multivariate Time Series. Eighth International Conference on Intelligent Systems Design and Applications, 2008. 8: p.432-437.
• 19. Voyant, C., Notton, G., Kalogirou, S., Nivet, M-L., Paoli, C., Motte, F., Fouilloy, A., Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 2017. 105: p. 569-582.
• 20. Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A. P., Machine learning for internet of things data analysis: a survey. Digital Communications and Networks, 2018. 4(3): p.161-175.
• 21. Rahman, A., Tasnim, S., Ensemble Classifiers and Their Applications: A Review. International Journal of Computer Trends and Technology, 2014. 10(1): p.31–35.
• 22. Kim, K-J., Cho, S. B., Ensemble classifiers based on correlation analysis for DNA microarray classification. Neurocomputing, 2006. 70: p.187-199.
• 23. Machine Learning Crash Course [Cited 2020 2 February]; Available from: https://developers.google.com/machine-learning/crash-course
• 24. Tshitoyan V. [Cited 2020 27 April]; Available from: https://www.github.com/vtshitoyan/plotConfMat