- Yongcai Wang, Xiaohong Hao, Lei Song, Chenye Wu, Yuexuan Wang, Changjian Hu, and Lu Yu. Tracking states of massive electrical appliances by lightweight metering and sequence decoding. In Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD '12). ACM, New York, NY, USA, 34-42, 2012.
- Nambi, S. N., Akshay, U., Papaioannou, Thanasis G., Chakraborty, Dipanjan, Aberer, Karl. Sustainable Energy Consumption Monitoring in Residential Settings. 2nd IEEE INFOCOM Workshop on Communications and Control for Smart Energy Systems, Turin, Italy, April 14-19, 2013.
However, I can't help but feel that any intrusion beyond the installation of a household-level monitor prevents the scalability of this technology. I accept that 100% disaggregation accuracy for a previously unseen household with a single aggregate meter is likely be to impossible. However, I find software solutions far more compelling than hardware solutions, due to their ability to scale to any data set containing a single stream of aggregate readings. Furthermore, I think the state of the art in unsupervised NIALM is still far from the best that can be achieved, and there are still many algorithmic improvements to be made.