It’s a good question. Ultimately in machine learning, the eventual algorithms are determined by the real world dataset with embedded ground truth (feedback on the the right answer). I’m fairly certain that most of us haven’t signed up to have our data open-sourced. And even if we could try a model that we created (more on that later) on only our own data, to forestall data privacy issues, the ultimate model(s) would likely be fairly useless for homes outside our own.
Another option might be to enable user-built models, developed in whatever machine learning framework Sense is using to be inserted into the Sense environment trained/tested against their aggregated data set. That would require the users to have a deep understanding of the Sense development, dataset and validation environment, as well as someone to foot the bill for fairly expensive (computationally and monetarily) training runs.
Quite honestly, there are a number of open source machine learning frameworks and power disaggregation datasets available today for someone who wants to play around with the basic concepts. Look for information on REDD and BLUED
But I can’t see Sense open sourcing their customer dataset, nor a way to easily allow outsiders to build models in their environment for training and testing. Plus who would pay the outside developers computational bills ??