Tuesday, 12 February 2013

Residential vs Commercial/Industrial NIALM

I was recently asked how energy disaggregation in the residential sector compares to the commercial and industrial sectors, since the majority of academic research focuses on domestic energy consumption. This got me thinking about the major differences, and why academic research focuses on disaggregating household appliances, so I decided to collect my thoughts here.

Total energy consumption

As a general rule of thumb, I think of a commercial premises (e.g. a shop, office or restaurant) as consuming roughly an order of magnitude more energy than an average household. Clearly there's a huge variability here, but I think it's a fair assumption given that, in general, there are more people spending more time in these premises. I think industrial premises are bound to consume even more energy than commercial premises, given that a single type of machinery could quickly consume more energy than a household or shop could in a day. This would surely make energy disaggregation more attractive in these settings, given the greater potential for energy savings.

Variation of loads

I'd expect commercial and industrial premises to contain many more electrical loads compared to a household. As a result, it's likely that more loads will be run simultaneously, and also more duplicate appliances will exist within a single premises. This clearly increases the complexity of the disaggregation problem. However, I'd also expect these types of premises to show a higher correlation over daily and weekly cycles, and further similarities are likely to exist between chains of commercial premises. These differences clearly change the focus of the disaggregation problem, and would likely require different approaches to feature selection and parameter learning.

Cost/benefit ratio

Given the greater potential for energy savings, the scenario becomes quite different to that of household energy disaggregation. There's clearly an argument for greater investment in monitoring equipment, such as higher frequency sampling or sub-metering. It might even be financially viable to monitor each load individually in an industrial environment, since the cost of the energy monitor is much lower in comparison to the size of the load. This begs the question of whether NIALM is even necessary in such situations.

Motivation and access

In my opinion, the domestic scenario is made far more compelling by the smart meter deployments mandated by many governments around the world. This means that household electricity data will soon be available at huge scales, and therefore there will be huge potential for advances in disaggregation algorithms. Conversely, commercial and industrial premises owners probably care relatively little about the electricity costs, as a result of the small cost to their business compared to other factors. I think these points help to explain why academic research has so far been concentrated around energy disaggregation in residential settings.

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