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

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 26 Jun 2023Embargo end date: 01 Jan 2022Publisher:Association for the Advancement of Artificial Intelligence (AAAI)Journal:Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 15,545-15,550 (issn: 2159-5399, eissn: 2374-3468,

Authors: Muppasani, Bharath; Anand, Cheyyur Jaya; Appajigowda, Chinmayi; Srivastava, Biplav; Johri, Lokesh;

doi: 10.1609/aaai.v37i13.26843 , 10.48550/arxiv.2209.00987

arXiv: 2209.00987 , http://arxiv.org/abs/2209.00987

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

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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.

Related Organizations

University of South Carolina Lancaster
United States
University of South Carolina
United States
University of South Carolina Lancaster
United States

Keywords

Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing

2 Research products, page 1 of 1

PowerIoT-State-Identification software on GitHub
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nilmtk software on GitHub
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