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Energy Efficiency
Erschienen in: Energy Efficiency 1/2018

21.08.2017 | Original Article

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

verfasst von: Chuan Choong Yang, Chit Siang Soh, Vooi Voon Yap

Erschienen in: Energy Efficiency | Ausgabe 1/2018

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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.
Vorheriger Artikel A linear programming optimization model for optimal operation strategy design and sizing of the CCHP systems
Nächster Artikel Correction of load curves estimated by electrical appliances ownership surveys using mass memory meters

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Literatur
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.MathSciNetCrossRefMATH
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
Cochran, W. G. (1952). The χ 2 test of goodness of fit. The Annals of Mathematical Statistics, 23, 315–415.CrossRefMATH
Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.CrossRefMATH
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.
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.
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.CrossRef
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.CrossRef
Forman, G., & Cohen, I. (2004). Learning from little: Comparison of classifiers given little training. Knowledge Discovery in Databases: PKDD, 2004, 161–172.
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.CrossRef
Giri, S., Berges, M., & Rowe, A. (2013a). Towards automated appliance recognition using an EMF sensor in NILM platforms. Advanced Engineering Informatics, 27, 477–485.CrossRef
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.CrossRef
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.CrossRef
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.MathSciNetMATH
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRefMATH
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.
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.CrossRef
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.CrossRef
Wang, Z., & Zheng, G. (2012). Residential appliances identification and monitoring by a nonintrusive method. IEEE Transactions on Smart Grid, 3, 80–92.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
Zeifman, M., & Roth, K. (2011). Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics, 57, 76–84.CrossRef
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.CrossRef
Metadaten
Titel
A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency
verfasst von
Chuan Choong Yang
Chit Siang Soh
Vooi Voon Yap
Publikationsdatum
21.08.2017
Verlag
Springer Netherlands
Erschienen in
Energy Efficiency / Ausgabe 1/2018
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI
https://doi.org/10.1007/s12053-017-9561-0

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