An Efficient Sparse Coding-Based Data-Mining Scheme in Smart Grid
Abstract
With the availability of Smart Grid, disaggregation, i.e. decomposing a whole electricity signal into its component appliances has gotten more and more attentions. Now the solutions based on the sparse coding, i.e. the supervised learning algorithm that belongs to Non-Intrusive Load Monitoring (NILM) have developed a lot. But the accuracy and efficiency of these solutions are not very high, we propose a new efficient sparse coding-based data-mining (ESCD) scheme in this paper to achieve higher accuracy and efficiency. First, we propose a new clustering algorithm – Probability Based Double Clustering (PDBC) based on Fast Search and Find of Density Peaks Clustering (FSFDP) algorithm, which can cluster the device consumption features fast and efficiently. Second, we propose a feature matching optimization algorithm – Max-Min Pruning Matching (MMPM) algorithm which can make the feature matching process to be real-time. Third, real experiments on a publicly available energy data set REDD [1] demonstrate that our proposed scheme achieves a for energy disaggregation. The average disaggregation accuracy reaches 77% and the disaggregation time for every 20 data is about 10 s.
Keywords
Smart Grid Energy disaggregation Sparse coding Data miningNotes
Acknowledgment
This work is partially supported by China National Key Research and Development Program No. 2016YFB0800301.
References
- 1.Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, vol. 25, pp. 59–62 (2011)Google Scholar
- 2.Pinkas, B.: Cryptographic techniques for privacy-preserving data mining. SIGKDD Explor. Newsl. 4(2), 12–19 (2002)MathSciNetCrossRefGoogle Scholar
- 3.Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRefGoogle Scholar
- 4.Berges, M., Goldman, E., Matthews, H.S., Soibelman, L.: Learning systems for electric comsumption of buildings. In: ASCI International Workshop on Computing in Civil Engineering (2009)Google Scholar
- 5.Shaw, S.R., Abler, C.B., Lepard, R.F., et al.: Instrumentation for high performance nonintrusive electrical load monitoring. J. Sol. Energy Eng. 120(3), 224–230 (1998)CrossRefGoogle Scholar
- 6.Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D.: At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: 9th International Conference on Ubiquitous Computing (UbiComp 2007) (2007)Google Scholar
- 7.Shao, H., Marwah, M., Ramakrishnan, N.: 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 May 2012Google Scholar
- 8.Zhong, M., Goddard, N., Sutton, C.: Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation (2014). arXiv preprint: arXiv:1406.7665
- 9.Lange, H., Bergs, M.: Efficient inference in dual-emission FHMM for energy disaggregation. In: AAAI Workshop: AI for Smart Grids and Smart Buildings (2016)Google Scholar
- 10.Norford, L.K., Leeb, S.B.: Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energ. Build. 24, 51–64 (1996)CrossRefGoogle Scholar
- 11.Shaw, S.R., Leeb, S.B., Norford, L.K., Cox, R.W.: Nonintrusive load monitoring and diagnostics in power systems. IEEE Trans. Instrum. Meas. 57, 1445–1454 (2008)CrossRefGoogle Scholar
- 12.Gupta, S., Reynolds, M.S., Patel, S.N.: 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, pp. 139–148, 26–29 September 2010Google Scholar
- 13.Srinivasan, D., Ng, W., Liew, A.: Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Del. 21, 398–405 (2006)CrossRefGoogle Scholar
- 14.Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J.: Unsupervised disaggregation of low frequency power measurements. In: Proceedings of the 11th SIAM International Conference on Data Mining, Mesa, AZ, USA, 28–30 April 2011Google Scholar
- 15.Elhamifar, E., Sastry, S.: Energy disaggregation via learning powerlets and sparse coding. In: AAAI, pp. 629–635 (2015)Google Scholar
- 16.Kolter, J.Z., Batra, S., Ng, A.Y.: Energy disaggregation via discriminative sparse coding. In: Advances in Neural Information Processing Systems, pp. 1153–1161 (2010)Google Scholar
- 17.Gupta, M., Majumdar, A.: Nuclear norm regularized robust dictionary learning for energy disaggregation. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 677–681. IEEE (2016)Google Scholar
- 18.Kolter, J.Z., Jaakkola, T.: Approximate inference in additive factorial HMMs with application to energy disaggregation. J. Mach. Learn. Res. 22, 1472–1482 (2012)Google Scholar
- 19.Elhamifar, E., Sapiro, G., Sastry, S.S.: Dissimilarity-based sparse subset selection. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2182–2197 (2016)CrossRefGoogle Scholar
- 20.Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
- 21.Lee, H., Battle, A., Raina, R., et al.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems, pp. 801–808 (2007)Google Scholar
- 22.Hoyer, P.O.: Non-negative sparse coding. In: Proceedings of the 2002 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565. IEEE (2002)Google Scholar
- 23.Bao, C., Ji, H., Quan, Y., et al.: Dictionary learning for sparse coding: algorithms and convergence analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1356–1369 (2016)CrossRefGoogle Scholar
- 24.Du, X., Guizani, M., Xiao, Y., Chen, H.H.: Secure and efficient time synchronization in heterogeneous sensor networks. IEEE Trans. Veh. Technol. 57(4), 2387–2394 (2008)CrossRefGoogle Scholar
- 25.Hei, X., Du, X., Wu, J., Hu, F.: Defending resource depletion attacks on implantable medical devices. In: Proceedings of IEEE GLOBECOM 2010, Miami, Florida, USA, December 2010Google Scholar
- 26.Yao, X., Han, X., Du, X., Zhou, X.: A lightweight multicast authentication mechanism for small scale IoT applications. IEEE Sens. J. 13(10), 3693–3701 (2013)CrossRefGoogle Scholar
- 27.Xiao, Y., Rayi, V., Sun, B., Du, X., Hu, F., Galloway, M.: A survey of key management schemes in wireless sensor networks. J. Comput. Commun. 30(11–12), 2314–2341 (2007)CrossRefGoogle Scholar
- 28.Du, X., Xiao, Y., Chen, H.H., Wu, Q.: Secure cell relay routing protocol for sensor networks. Wirel. Commun. Mob. Comput. 6(3), 375–391 (2006)CrossRefGoogle Scholar
- 29.Du, X., Guizani, M., Xiao, Y., Chen, H.H.: A routing-driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks. IEEE Trans. Wirel. Commun. 8(3), 1223–1229 (2009)CrossRefGoogle Scholar