In this book, the Machine Learning approaches for Non-Intrusive Load Monitoring have been studied. Within all the techniques explored by the scientific community, this work has been focused on the hidden Markov model based and the deep neural network based, since their capability and promising performance at the forefront of the improvements could be introduced.
KeywordsConclusion Future works Performance improvement Gaussian mixture models Neural rest-of-the-world model
- 21.J.Z. Kolter, T. Jaakkola, Approximate inference in additive factorial HMMs with application to energy disaggregation. J. Mach. Learn. Res. 22, 1472–1482 (2012)Google Scholar
- 29.J.Z. Kolter, M.J. Johnson, REDD: a public data set for energy disaggregation research, in Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability, San Diego (2011), pp. 1–6Google Scholar
- 31.J. Kelly, W. Knottenbelt, Neural NILM: deep neural networks applied to energy disaggregation, in Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, BuildSys ’15 (ACM, New York, 2015), pp. 55–64Google Scholar
- 58.S. Makonin, F. Popowich, L. Bartram, B. Gill, I.V. Bajic, AMPds: a public dataset for load disaggregation and eco-feedback research, in Proceedings of the IEEE Electrical Power and Energy Conference (EPEC), Halifax (2013)Google Scholar