Imperial MSc Computing Science students do a 3-month individual project over summer. Below is a proposal I and my Ph.D. supervisor have just submitted. Of course, there are no guarantees that any students will be interested…
“Inferring appliance-by-appliance energy consumption from whole-house electricity meter readings”
By the end of the decade, every house in the UK will have a “smart meter” installed. Each smart meter will record the electricity consumption for the whole house once every ten seconds.
There is good evidence that people find appliance-by-appliance information to be considerably more useful than whole-house aggregate information when making decisions about saving energy. Hence it would be very useful to be able to disaggregate whole-house electricity meter signals into appliance-by-appliance information.
The aim of this project is to implement a disaggregation algorithm and evaluate its performance against real data. The design of the disaggregation algorithm can be your own invention or an algorithm already described in the literature. There are many approaches to this problem and you will be free to choose an approach.
We have a dataset recorded from multiple houses over several months (for each house we recorded the whole-house current and voltage waveforms at 8kHz as well as the “ground truth” of how much power individual appliances are actually using). We also have funding to install meters in your own home, if you wish.
For the “classic” paper on this topic, see:
G. W. Hart, ‘Nonintrusive appliance load monitoring’, Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, Dec. 1992. DOI:10.1109/5.192069
For a recent review of the literature, see:
K. C. Armel, A. Gupta, G. Shrimali, and A. Albert, ‘Is disaggregation the holy grail of energy efficiency? The case of electricity’, Energy Policy, vol. 52, pp. 213 – 234, 2013. DOI:10.1016/j.enpol.2012.08.062