A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors
This work addresses the challenge of non-intrusive power sensing for usage state identification, but it is incremental as it primarily offers a dataset and baseline rather than a novel method.
The authors tackled the problem of identifying power usage patterns in buildings and factories by releasing a dataset from 8 institutions across the US and India and providing an unsupervised machine learning baseline solution to accelerate research in this area.
The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial un-supervised machine learning based solution as a baseline for the community to accelerate research in this area.