ML Anthology

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
AAAI 2023 pp. 15545-15550
doi:10.1609/AAAI.V37I13.26843 /aaai/2023/muppasani2023aaai-dataset/

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.

PDF AAAI Semantic Scholar

Cite

Text

Muppasani et al. "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing with MiDAS IoT-Based Sensors." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26843

Markdown

[Muppasani et al. "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing with MiDAS IoT-Based Sensors." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/muppasani2023aaai-dataset/) doi:10.1609/AAAI.V37I13.26843

BibTeX

@inproceedings{muppasani2023aaai-dataset,
  title     = {{A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing with MiDAS IoT-Based Sensors}},
  author    = {Muppasani, Bharath and Anand, Cheyyur Jaya and Appajigowda, Chinmayi and Srivastava, Biplav and Johri, Lokesh},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {15545-15550},
  doi       = {10.1609/AAAI.V37I13.26843},
  url       = {https://mlanthology.org/aaai/2023/muppasani2023aaai-dataset/}
}