Building up the motivation -II

In my last post, I provided a summary of all the work that has been done so far in accessing the potential savings from appliance-wise disaggregation. All the studies were done for a period of 2-4 months, which is not enough time to estimate savings arising from behavioral changes (that time frame isn’t even enough to account for weather related changes!). All the studies had 10-20 houses (except for Karbo-Larsen study, which provides very little detail)- which is a very limited sample size. But given the overhead cost and maintenance issues related to monitoring all the appliances in a house, the small sample sizes are no surprise. In summary, the studies done so far in this field claim savings of 10-15% based on limited sample size and trial duration.

Following is the list of things that are of importance in a study like this, that hasn’t been tackled at all in the literature pertaining to NILM advantages. I believe they are of fundamental importance in motivating the solution to the NILM problem, and might even play crucial role in policy making.

1.  The kind of behavioral changes that disaggregated feedback can invoke. For instance, are people more likely to change/ replace appliances or are they more likely to change the way they use their appliances (habits).

2. In terms of behavioral changes, what kind of changes are more likely. This gets to the heart of what appliances really need to identified.

3. What is the minimum number of appliances (and which ones) that need to be given feedback on to achieve savings. i.e after how many appliances does the marginal savings begin to decrease. This information is fundamental for algorithm development.

4.  How does the demographic (income, age, household size etc.) affect what kind of behavioral changes are observed.

5. What is the estimated savings based on what people can change (based on their demographics).

6. Are the changes/ savings persistent ?

7. Is disaggregated feedback required infinitely, or does the savings from it saturate after some time after which it is no longer required ?

8. Is appliance level feedback the only kind of disaggregated feedback that can generate savings ? What about feedback in terms of what room is consuming how much, or what activity (within some broadly defined categories) is consuming how much. [This opens up a whole new avenue of research].

A study that addresses these issues will not only get to the heart of the actual implications of disaggregated feedback, but will also motivate other ways of tackling the same problem.

7 thoughts on “Building up the motivation -II

  1. It would be worth mentioning somewhere that disaggregated power consumption information is not only useful for human feedback + energy savings, and that there are other use cases that may still benefit from this information.

  2. Hi Suman,
    A conflict of interest indeed! However, I think such large scale studies are non-trivial for academicians.
    Glad you like nilmtk. More to come soon.

    Aside: I didn’t get any notification regarding your follow up comment. Maybe you may wish to enable that.

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