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Jack Kelly
Jan 20, 2015, 3:41:42 PM1/20/15
to energy-dis...@googlegroups.com
I have a wonderful group of MSc students working on building a proof-of-concept energy disaggregation competition platform. The full project spec is here.
The basic idea is that the platform will allow researchers to download training data (recording both the whole-house aggregate and individual appliance data) at a number of different sample rates, and then researchers will upload the output of their disaggregation algorithm to the platform for scoring against a set of metrics.
Would it be useful to have different categories of NILM algorithm? e.g. distinguish between algorithms which can generalise across houses and those "toy" (but still interesting) algorithms which must be trained and tested on the same house? Or should we just ignore such "toy" algorithms? And should we have separate categories for high freq (defined as > 1 Hz ???) and low freq algorithms?
The basic idea is that the platform will allow researchers to download training data (recording both the whole-house aggregate and individual appliance data) at a number of different sample rates, and then researchers will upload the output of their disaggregation algorithm to the platform for scoring against a set of metrics.
Would it be useful to have different categories of NILM algorithm? e.g. distinguish between algorithms which can generalise across houses and those "toy" (but still interesting) algorithms which must be trained and tested on the same house? Or should we just ignore such "toy" algorithms? And should we have separate categories for high freq (defined as > 1 Hz ???) and low freq algorithms?
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