Report from NILM2014@London on comparing NILM algorithms

The first "NILM in London" workshop was held on Wednesday 3rd September. In this blog post, I'd like to try to summarise the discussion around comparing NILM algorithms.

At present, it is very hard (if not impossible) to objectively compare any two NILM algorithms. This is true for both academia and industry. The problem is that each research paper tends to use a different dataset, different metrics, different appliances etc. The situation improved considerably in 2011 with the release of the REDD dataset. But we are still some distance from being able to directly compare the performance of any pair of NILM algorithms.

Oli Parson lead a brief session on NILM evaluation. He presented a great summary of the Belkin Energy Disaggregation competition on Kaggle.

Several points were raised during the discussion:

  • Dominik Egarter raised the point: How do we compare NILM algorithms with fundamentally different assumptions and inputs? For example, say algorithm A requires that the use list every appliance in the home but algorithm B requires no information from the user. Algorithm A gives an accuracy of 85% whilst algorithm B gives an accuracy of 75% percent. Which is better? Should the algorithm which requires more information be penalised in some way? Is it even fair to directly compare them? Should we define a set of 'NILM algorithm classes' and only compare algorithms within their own class? We could come up with a set of 'NILM algorithm classes' by considering specific scenarios and use-cases. For example, most domestic users probably won't be bothered to enumerate every appliance in their home, so we could have a 'zero user input' class (which does not necessarily mean 'unsupervised' in the machine learning sense because the system could access generic appliance models trained from, say, the public datasets).

  • Companies offering NILM are focussed on offering a NILM service which is satisfies their particular users' needs. They might see very little value in having a global 'leader board' of performance. For example, when you hire a builder to modify your house, you don't consult some regional league table of builders. Instead you find the local builder who can offer you everything you need, and you really don't care if they are a few percentage points behind some other local builders on some particular metric.

  • Which metric(s) to use to compare NILM algorithms? We probably need to use multiple metrics

  • Do we need to spend lots of money collecting a 'validation dataset'. The idea being that, if we are trying to validate commercial NILM services, then we probably need to keep the test data private (so people don't cheat!) But collecting a large dataset is very expensive. If companies are not interested in a 3rd party NILM validation tool then perhaps we do not need to bother to collect a new dataset. Instead, if only academics are interested in competing on a public 'leader board' then we probably don't need a private dataset, especially if academics are encouraged to release their code. Computer Vision competitions like the ImageNet Large Scale Visual Recognition Challenge use public data (I think).

  • However, companies might well be interested in privately assessing how well their algorithms perform relative to some benchmark (and/or the academic state of the art).

  • In terms of 'benchmarks', it might be nice to explore how each metric responds to 'naive' approaches. e.g. some metrics will give surprisingly high 'marks' if you just predict that all appliances are 'off' all the time! Or using simple 'simulation' using just probability density functions of each appliance for each time of day.

(I didn't take notes at the meeting so I have probably forgotten some points. Please add anything I've missed / garbled to the comments!)


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