AAAI 2023technical1 citations
A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors
Bharath Muppasani, Cheyyur Jaya Anand, Chinmayi Appajigowda, Biplav Srivastava, Lokesh Johri
Abstract
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 unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.
BibTeX
@article{Muppasani_Anand_Appajigowda_Srivastava_Johri_2024, title={A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26843}, DOI={10.1609/aaai.v37i13.26843}, abstractNote={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 unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Muppasani, Bharath and Anand, Cheyyur Jaya and Appajigowda, Chinmayi and Srivastava, Biplav and Johri, Lokesh}, year={2024}, month={Jul.}, pages={15545-15550} }