A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors

Authors

  • Bharath Muppasani University of South Carolina
  • Cheyyur Jaya Anand Tantiv4
  • Chinmayi Appajigowda Tantiv4
  • Biplav Srivastava University of South Carolina
  • Lokesh Johri Tantiv4

DOI:

https://doi.org/10.1609/aaai.v37i13.26843

Keywords:

IoT, Time Series Data, Clustering, Electrical Device Harmonics

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.
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Published

2024-07-15

How to Cite

Muppasani, B., Anand, C. J., Appajigowda, C., Srivastava, B., & Johri, L. (2024). A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15545-15550. https://doi.org/10.1609/aaai.v37i13.26843

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

IAAI Technical Track on deployed Highly Innovative Applications of AI