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A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

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Abstract

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.

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References

  • Alasalmi, T., Suutala, J., Röning, J., (2012). Real-Time Non-Intrusive Appliance Load Monitor - Feedback System for Single-point per Appliance Electricity Usage. In Proceedings of SMARTGREENS 2012 - International Conference on Smart Grids and Green IT Systems, SciTePress, 2012, pp. 203–208.

  • Anderson, K., Berges, M., Ocneanu, A., Benitez, D., Moura, J.M.F., (2012). Event detection for non intrusive load monitoring. In Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society (IECON), Montreal, Canada, 25–28, pp. 3312–3317.

  • Arias-Castro, E., & Donoho, D. L. (2009). Does median filtering truly preserve edges better than linear filtering? Annals of Statistics, 37, 1172–1206.

    Article  MathSciNet  MATH  Google Scholar 

  • Ashari, A., Paryudi, I., & Tjao, A. M. (2013). Performance comparison between Naïve Bayes, decision tree and k-nearest neighbor in searching alternative design in an energy simulation tool. International Journal of Advanced Computer Science and Applications, 4(11), 33–39.

    Article  Google Scholar 

  • Baets, L. D., Ruyssinck, J., Develder, C., Dhaene, T., & Deschrijver, D. (2017). On the Bayesian optimization and robustness of event detection methods in NILM. Energy and Buildings, 145, 57–66.

    Article  Google Scholar 

  • Barker, S., Musthag, M., Irwin, D., Shenoy, P. (2014). Non-Intrusive Load Identification for Smart Outlets, in: Proceedings of the 5th IEEE International Conference on Smart Grid Communications (SmartGridComm 2014), Venice, Italy, 3–6 November 2014.

  • Basu, K. (2014). Classification techniques for non-intrusive load monitoring and prediction of residential loads. Electric power. Université de Grenoble. English. <NNT: 2014GRENT089>.<tel-01162610>.

  • Basu, K., Debusschere, V., Bacha, S., Maulik, U., Bondyopadhyay, S. (2015a). Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach, IEEE Transactions On Industrial Informatics, Vol. 11, No. 1.

  • Basu, K., Debusscherea, V., Douzal-Chouakria, A., & Bacha, S. (2015b). Time series distance-based methods for non-intrusive load monitoring in residential buildings. Energy and Buildings, 96, 109–117.

    Article  Google Scholar 

  • Behavioural Insights Team. (2011). Behaviour change and energy use. London: Cabinet Office (Available at) https://www.gov.uk/government/publications/behaviour-change-and-energy-use-behavioural-insights-team-paper [online].

    Google Scholar 

  • Berges, M., Goldman, E., Matthews, H.S., Soibelman, L. (2009). Learning Systems for Electric Consumption of Buildings. In Proceedings of ASCE International Workshop on Computing in Civil Engineering, Austin, TX, USA, 24–27.

  • Berges, M., Goldman, E., Matthews, H. S., & Soibelman, L. (2010). Enhancing electricity audits in residential buildings with nonintrusive load monitoring. Journal of Industrial Ecology, 14, 844–858.

    Article  Google Scholar 

  • Berges, M., Goldman, E., Matthews, H. S., Soibelman, L., & Anderson, K. (2011). User-centered non-intrusive electricity load monitoring for residential buildings. Journal of Computing in Civil Engineering, 25, 471–480.

    Article  Google Scholar 

  • Carrie Armel, K., Gupta, A., Shrimali, G., & Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? The case of electricity. Energ Policy, 52, 213–234.

    Article  Google Scholar 

  • Chahine, K., Drissi, K., Pasquier, C., Kerrroum, K., Faure, C., Jounnet, T., Michou, M. (2011). Electric load disaggregation in smart metering using a novel feature extraction method and supervised classification. In Proceedings of MEDGREEN 2011, Beirut, Lebanon, Energy Procedia, 6, pp. 627–632.

  • Chang, H. H. (2012). Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies, 5, 4569–4589.

    Article  Google Scholar 

  • Chang, H.H., Yang, H.T., Lin, C.L. (2008). Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. In Computer Supported Cooperative Work in Design IV; Springer: Berlin, Germany, pp. 664–674.

  • Chang, H.H., Lin, C.L., Lee, J.K. (2010). Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms. In Proceedings of the 2010 14th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Shanghai, China, 14–16, pp. 27–32.

  • Chang, H. H., Lian, K. L., Su, Y. C., & Lee, W. J. (2014). Power Spectrum-based wavelet transform for non-intrusive demand monitoring and load identification. IEEE Transactions on Industry Applications, 50, 2081–2089.

    Article  Google Scholar 

  • Cochran, W. G. (1952). The χ 2 test of goodness of fit. The Annals of Mathematical Statistics, 23, 315–415.

    Article  MATH  Google Scholar 

  • Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.

    Article  MATH  Google Scholar 

  • Darby, S. (2006). The effectiveness of feedback on energy consumption: A review for DEFRA of the literature on metering, billing and direct displays. Oxford: Environmental Change Institute, University of Oxford.

    Google Scholar 

  • Davis, J., Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 25-29, pp. 233–240.

  • Duda R.O., Hart P.E., Stork D.G. (2000). Pattern Classification, second ed., Wiley-Interscience, pp. 182–187.

  • Ehrhardt-Martinez, K., Donnelly, K. A., & Laitner, J. A. (2010). Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities. Technical Reports E105. Washington, DC: American Council for an Energy-Efficient Economy.

    Google Scholar 

  • European Environment Agency (2013). 'Achieving energy efficiency through behaviour change: what does it take?', Technical report No 5/2013, (Available at) http://www.eea.europa.eu/publications/achieving-energy-efficiency-through-behaviour [online].

  • Faruqui, A., Sergici, S., & Sharif, A. (2010). The impact of informational feedback on energy consumption—A survey of the experimental evidence. Energy, 35(4), 1598–1608.

    Article  Google Scholar 

  • Figueiredo M., de Almeida A., Ribeiro B. (2011). An experimental study on electrical signature identification of Non-Intrusive Load Monitoring (NILM) systems In Adaptive and Natural Computing Algorithms, Springer, Berlin, Germany, Volume 6594, pp. 31–40.

  • Fischer, C. (2008). Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency, 1(1), 79–104.

    Article  Google Scholar 

  • Forman, G., & Cohen, I. (2004). Learning from little: Comparison of classifiers given little training. Knowledge Discovery in Databases: PKDD, 2004, 161–172.

    Google Scholar 

  • Froehlich J. (2009). "Promoting Energy Efficient Behaviors in the Home through Feedback: The Role of Human-Computer Interaction," Human Computer Interaction Consortium 2009 Winter Workshop.

  • Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds, M., & Patel, S. (2011). Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Computing, 10, 28–39.

    Article  Google Scholar 

  • Giri, S., Berges, M., & Rowe, A. (2013a). Towards automated appliance recognition using an EMF sensor in NILM platforms. Advanced Engineering Informatics, 27, 477–485.

    Article  Google Scholar 

  • Giri, S., Lai, P., Berges, M. (2013b). Novel Techniques For ON and OFF states detection of appliances for Power Estimation in Non-Intrusive Load Monitoring. In Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC), Montreal, Canada, pp. 522–530.

  • Goncalves, H., Ocneanu, A., Berges, M., Fan, R.H. (2011). Unsupervised Disaggregation of Appliances Using Aggregated Consumption Data. In Proceedings of KDD 2011 Workshop on Data Mining Applications for Sustainability, San Diego, CA, USA, 21–24 August 2011.

  • Gupta, S., Reynolds, M.S., Patel, S.N. (2010). ElectriSense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, 26–29, pp. 139–148.

  • Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80, 1870–1891.

    Article  Google Scholar 

  • Jazizadeh, F., Becerik-Gerber, B., Berges, M., & Soibelman, L. (2014). An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems. Advanced Engineering Informatics, 28, 311–326.

    Article  Google Scholar 

  • Jin, Y., Tebekaemi, E., Berges, M., Soibelman, L. (2011). Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 22–27, pp. 4340–4343.

  • Johnson, M. J., & Willsky, A. S. (2013). Bayesian nonparametric hidden semi- Markov models. Journal of Machine Learning Research, 14(1), 673–701.

    MathSciNet  MATH  Google Scholar 

  • Kato, T., Cho, H.S., Lee, D. (2009). Appliance recognition from electric current signals for information-energy integrated network in home environments. In Proceedings of the 7th International Conference on Smart Homes and Health Telematics, Tours, France, 1–3 July 2009, Volume 5597, pp. 150–157.

  • Kelly J, Knottenbelt W, Neural NILM (2015). Deep Neural Networks Applied to Energy Disaggregation, Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, November 04–05, 2015, Seoul, South Korea.

  • Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 11th SIAM international conference on data mining, mesa, AZ, USA, 28–30, pp. 747–758.

  • Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine Learning, 30(2/3), 271–274.

    Article  Google Scholar 

  • Kolter, J.Z., Jaakkola, T. (2012). Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1472–1482.

  • Kolter, J.Z., Johnson, M.J. (2011). REDD: A Public Data Set for Energy Disaggregation Research. In Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability, San Diego,CA, USA, pp. 1–6.

  • Kramer, O., Wilken, O., Beenken, P., Hein, A., Klingenberg, T., Meinecke, C., Raabe, T., Sonnenschein, M. (2012). On Ensemble Classifiers for Nonintrusive Appliance Load Monitoring. In Proceeding of the 7th international conference on Hybrid Artificial Intelligent Systems, Springer-Verlag, Berlin, Germany, Part I, pp. 322–331.

  • Li, J., West, S., Platt, G. (2012). Power decomposition based on SVM regression. In Proceedings of International Conference on Modelling, Identification Control, Wuhan, China, 24–26, pp. 1195–1199.

  • Li, Y., Peng, Z., Huang, J., Zhang, Z, Jae, H.S. (2014). Energy Disaggregation via Hierarchical Factorial HMM. In Proceedings of the 2nd International Workshop on Non-Intrusive Load Monitoring (NILM).

  • Lin, G.Y., Lee, S.C., Hsu, Y.J., Jih, W.R. (2010a). Applying power meters for appliance recognition on the electric panel. In Proceedings of the 5th IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia, 15–17, pp. 2254–2259.

  • Lin, G.Y., Lee, S.C., Hsu, Y.J., Jih, W.R. (2010b). Applying power meters for appliance recognition on the electric panel. In Proceedings of the 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), Taichung, Taiwan, 15–17, pp. 2254–2259.

  • Makonin, S., & Popowich, F. (2015). Nonintrusive load monitoring (NILM) performance evaluation. Energy Efficiency, 8(4), 809–814.

    Article  Google Scholar 

  • Makonin, S.; Popowich, F.; Bajic, I.V.; Gill, B.; Bartram, L. (2015). "Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring," in Smart Grid, IEEE Transactions on, vol. PP, no.99, pp.1–11.

  • Marceau, M. L., & Zmeureanu, R. (2000). Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Conversion and Management, 41, 1389–1403.

    Article  Google Scholar 

  • Marchiori, A., Han, Q. (2009). Using Circuit-Level Power Measurements in Household Energy Management Systems. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, (BuildSys 09), ACM Press, pp. 7–12.

  • Marchiori, A., Hakkarinen, D., Han, Q., & Earle, L. (2011). Circuit-level load monitoring for household energy management. IEEE Pervasive Computing, 10, 40–48.

    Article  Google Scholar 

  • Meehan, P., McArdle, C., & Daniels, S. (2014). An efficient, scalable time-frequency method for tracking energy usage of domestic appliances using a two-step classification algorithm. Energies, 7, 7041–7066.

    Article  Google Scholar 

  • Parson, O., Ghosh, S., Weal, M. J., & Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Artificial Intelligence, 217, 1–19.

    Article  MATH  Google Scholar 

  • Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D. (2007). At the Flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of the 9th International Conference on Ubiquitous Computing, Innsbruck, Austria, 16–19, pp. 271–288.

  • Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, Informedness, Markedness and correlation. Journal of Machine Learning Technologies, 2, 37–63.

    Google Scholar 

  • Rahimi, S., Chan, A.D.C., Goubran, R.A. (2012). Nonintrusive load monitoring of electrical devices in health smart homes. In Proceedings of IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Graz, 13-16, pp. 2313–2316.

  • Roos, J., Lane, I., Botha, E., Hancke, G. (1994). Using neural networks for non-intrusive monitoring of industrial electrical loads. In Proceedings of the 10th IEEE Instrumentation and Measurement Technology Conference, Hamamatsu; IEEE: 10-12, pp. 1115–1118.

  • Saitoh, T., Aota, Y., Osaki, T., Konishi, R., & Sugahara, K. (2010). Current sensor based home appliance and state of appliance recognition. SICE Journal of Control, Measurement, and System Integration, 3, 86–93.

    Article  Google Scholar 

  • Salperwyck, C., Lemaire, V. (2011). Learning with few examples: An empirical study on leading classifiers. In Proceedings of The 2011 International Joint Conference on Neural Networks (IJCNN 2011), San Jose, California, USA, pp. 1010–1019.

  • Shao, H., Marwah, M., Ramakrishnan, N. (2012). A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation. In Proceedings of the 1st International Workshop on Non-Intrusive Load Monitoring, Pittsburgh, PA, USA, 7.

  • Spiegel, S., Albayrak, S. (2014). Energy disaggregation meets heating control. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, Republic of Korea, 24–28.

  • Srinivasan, D., Ng, W., & Liew, A. (2006). Neural-network-based signature recognition for harmonic source identification. IEEE Transactions on Power Delivery, 21, 398–405.

    Article  Google Scholar 

  • Wang, Z., & Zheng, G. (2012). Residential appliances identification and monitoring by a nonintrusive method. IEEE Transactions on Smart Grid, 3, 80–92.

    Article  Google Scholar 

  • Wong, Y.F., Ahmet Sekercioglu, Y., Drummond, T., Wong, V.S. (2013). Recent approaches to non-intrusive load monitoring techniques in residential settings. In Proceedings of the 2013 I.E. symposium on computational intelligence applications in smart grid (CIASG), Singapore, 16-19, pp. 73–79.

  • Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Philip, S. Y., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1–37.

    Article  Google Scholar 

  • Yang, C.C., Soh, C.S., Yap, V.V. (2014). Comparative Study of Event Detection Methods for Non-intrusive Appliance Load Monitoring. In Proceedings of the 6th International Conference on Applied Energy (ICAE2014), Taipei City, Taiwan, 30 May – 2 June 2014, Energy Procedia, pp. 1840–1843.

  • Yang, C. C., Soh, C. S., & Yap, V. V. (2015). A systematic approach to ON-OFF event detection and clustering analysis for non-intrusive appliance load monitoring. Frontiers in Energy, Frontiers in Energy, 9(2), 231–237.

    Article  Google Scholar 

  • Yang, C. C., Soh, C. S., & Yap, V. V. (2016). A Systematic Approach in Load Disaggregation Utilizing a Multi-Stage Classification Algorithm for Consumer Electrical Appliances Classification. Frontiers in Energy, Springer, (Accepted 12.October. 2016). doi:10.1007/s11708-017-0497-z.

  • Yang, C. C., Soh, C. S., & Yap, V. V. (2017). A non-intrusive appliance load monitoring for efficient energy consumption based on naive Bayes classifier. Sustainable Computing: Informatics and Systems, 14, 34–42.

    Google Scholar 

  • Yoshimoto, K., Nakano, Y., Amano, Y., Kermanshahi, B. (2000). Non-intrusive appliances load monitoring system using neural networks. In ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 20-25, pp. 183–194.

  • Zeifman, M. (2012). Disaggregation of home energy display data using probabilistic approach. IEEE Transactions on Consumer Electronics, 58, 23–31.

    Article  Google Scholar 

  • Zeifman, M., & Roth, K. (2011). Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics, 57, 76–84.

    Article  Google Scholar 

  • Zeifman, M., Roth, K., Stefan, J. (2013). Automatic recognition of major end-uses in disaggregation of home energy display data. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 11-14, pp. 104–105.

  • Zhou, Z.H. , Zhan, D.C. , Yang, Q (2007). Semi-supervised learning with very few labeled training examples, Proceedings of the 22nd national conference on Artificial intelligence, p.675–680, July 22–26, 2007, Vancouver, British Columbia, Canada.

  • Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12, 16838–16866.

    Article  Google Scholar 

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Acknowledgements

The work is funded by Ministry of Science, Technology and Innovation (MOSTI) Malaysia under the MOSTI Science fund project (No. 06-02-11-SF0162).

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  1. Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900, Kampar, Perak, Malaysia

    Chuan Choong Yang, Chit Siang Soh & Vooi Voon Yap

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Yang, C.C., Soh, C.S. & Yap, V.V. A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency 11, 239–259 (2018). https://doi.org/10.1007/s12053-017-9561-0

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