Advertisement

Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators

  • Michael Mayo
  • Sara Omranian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9794)

Abstract

In this paper we present some preliminary work towards the development of a new evolutionary subsampling technique for solving the non-intrusive load monitoring (NILM) problem. The NILM problem concerns using predictive algorithms to analyse whole-house energy usage measurements, so that individual appliance energy usages can be disaggregated. The motivation is to educate home owners about their energy usage. However, by their very nature, the datasets used in this research are massively imbalanced in their target value distributions. Consequently standard machine learning techniques, which often rely on optimising for root mean squared error (RMSE), typically fail. We therefore propose the target-weighted RMSE (TW-RMSE) metric as an alternative fitness function for optimising load disaggregators, and show in a simple initial study in which random search is utilised that TW-RMSE is a metric that can be optimised, and therefore has the potential to be included in a larger evolutionary subsampling-based solution to this problem.

Keywords

Non-intrusive load monitoring Disaggregation Imbalanced regression Fitness function Evolutionary undersampling 

References

  1. 1.
    Branco, P., Torgo, L., Ribeiro, R.: A survey of predictive modelling under imbalanced distributions (2015). arXiv:1505.01658v2
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)zbMATHGoogle Scholar
  4. 4.
    Derrac, J., Garcia, S., Herrera, F.: A survey of evolutionary instance selection and generation. Int. J. Appl. Metaheuristic Comput. 1(1), 60–92 (2010)CrossRefGoogle Scholar
  5. 5.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRefGoogle Scholar
  6. 6.
    Hernández-Orallo, J.: ROC curves for regression. Pattern Recogn. 46(12), 3395–3411 (2013)CrossRefzbMATHGoogle Scholar
  7. 7.
    Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajic, I.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: 2013 IEEE Electrical Power Energy Conference (EPEC), pp. 1–6 (2013)Google Scholar
  8. 8.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  9. 9.
    Ribeiro, R.: Utility-based regression. Ph.D. thesis, Department of Computer Science, Faculty of Sciences, University of Porto (2011)Google Scholar
  10. 10.
    Torgo, L., Ribeiro, R.: Utility-based regression. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 597–604. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Vine, D., Buys, L., Morris, P.: The effectiveness of energy feedback for conservation and peak demand: a literature review. Open J. Energy Effic. 2, 7–15 (2013)CrossRefGoogle Scholar
  12. 12.
    Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57, 76–84 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations