Friday 23 August 2013

Incorporating general appliance knowledge into disaggregation algorithms

I've recently been thinking a lot about how to incorporate prior knowledge into disaggregation training algorithms. By prior knowledge, I mean general information about how appliance types operate, i.e. what makes a fridge different from a washing machine. However, this prior knowledge should not be specific to a single household, and should therefore generalise to previously unseen households. Clearly, such prior knowledge is required if a NIALM system is to operate without manual intervention. To date, I have seen two categories of approaches which incorporate prior information into the learning process, which I have described below.

Sequential learning and labelling

This approach first aims to identify the characteristics of the appliances in a household without any prior knowledge. This produces a list of appliances (e.g. appliance 1, appliance 2) with their corresponding behaviour. Next, this category of approaches use a second step to assign labels to the learned appliances (e.g. appliance 1 = fridge, appliance 2 = washing machine). A diagram of this approach is given below:

This approach has been adopted in some recent state-of-the-art disaggregation papers. Both Kim et al. (2011) and Kolter et al. (2012) both use an unsupervised learning approach to the first step, while the manual labelling of learned appliance models is required by the second step.

Simultaneous learning and labelling

This approach aims to use both aggregate data and prior knowledge in order to simultaneously learn appliance models, as shown below:

This seems to be a more principled approach, in that prior knowledge is not ignored when the learning algorithm identifies distinct appliances within the aggregate load. We demonstrated such an approach in a recent paper (Parson et al., 2012). Furthermore, this type of approach lends itself well to Bayesian learning, whereby the learned appliances models constitute a weighted combination of information extracted from aggregate data and the general appliance models. Such an approach is detailed in Johnson and Willsky (2013).

In summary, I believe it is essential to incorporate such general appliance knowledge into the learning algorithms of energy disaggregation systems to allow them to scale to large numbers of previously unseen households. Only in the past few years has published work come close to Hart's vision of a Manual Setup NIALM (Hart, 1992), but the problem is still far from solved.

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