PowerIoT-State-Identification

Repository for details about the dataset shared in our paper "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors"

1. The code folder contains the python notebook with documentation of the different methods used in our approach.
2. The doc folder contains our publication.
3. The data folder contains
   a. location_states.json: file contains the details about the states identified for each location and the respective centers for each state.
   b. State Summary Validation.csv: Validation details of the identified states for each location.
4. The leaderboard folder contains
   a. f1_scores.json: This file contains the model performance for each test date in all the locations of the released dataset.
   b. paper_f1_scores.json: The f1_score details of the test dates presented in our paper can be found in this file.
   c. The step-by-step details to reproduce the results presented in our paper can be found here
5. The metadata folder contains the metadata for both power and harmonics datasets
6. The results folder contains the graphs with the identified states using our approach for each location in the release dataset.

Obtaining the dataset

Please fill the following Google form to get a link to the dataset - https://forms.gle/cHJdq7a56GuAEd4N6
Additional data for more days for the same locations presented in our paper from January-August 2022 can be requested for research purposes by contacting the authors.

Citations

If you are using this data, please cite.

@inproceedings{midas-state-id,

author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda  and Biplav Srivastava  and Lokesh Johri},

title = {A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors},

booktitle = {Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23)},
year = {2023},
keywords = {Signal Processing (eess.SP), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences},
copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}

}

Other works based on this data are for forecasting and interacting with it using a chatbot.

@inproceedings{midas-forecasting,
author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda  and Biplav Srivastava  and Lokesh Johri},
title = {Power Forecasting and Anomaly Detection with MIDAS IoT-based Sensor},
year = {2022},
booktitle = {DOI: 10.13140/RG.2.2.17358.33600},
}

@inproceedings{nl2sql,
author = {Lakkaraju, Kausik and Palaiya, Vinamra and Paladi, Sai Teja and Appajigowda, Chinmayi and Srivastava, Biplav and Johri, Lokesh},
title = {Data-Based Insights for the Masses: Scaling Natural Language Querying to Middleware Data},
year = {2022},
booktitle = {Proc. Database Sys.  Adv. App. (DASFAA)},
keywords = {Middleware, Natural language query, Chatbots}
}

License

Creative Commons Attribution 4.0 International

This is a collaborative work with Tantiv4 and University of South Carolina, Columbia.