@inproceedings{f3e3ea1c07f34775ac6d5a342cb37e61,
title = "Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity",
abstract = "In the past decade, a plethora of efforts were given to the field of facilitating a better smart grid system by leveraging the power of artificial intelligence. Undoubtedly, machine learning is currently playing an increasingly important role in almost every aspect of power systems. However, in real practice, there is a much larger amount of unlabelled data than the one labelled by human experts. In this work, we make a perspective study on overcoming the data scarcity in smart grid. The active learning strategy will be proposed to provide a feasible solution for addressing the data scarcity challenge. In addition, we will give a discussion on current state-of-the-art and the limitations in previous work. We hope this work can be a good guide for researchers to further the relevant study in the near future.",
keywords = "Active learning, Machine Learning, Power Systems, Smart Grid, Weakly Supervised Learning",
author = "Wei Guo and Xiang Zha and Kun Qian and Tao Chen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd IEEE International Conference on Electronics and Communication Engineering, ICECE 2019 ; Conference date: 09-12-2019 Through 11-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICECE48499.2019.9058539",
language = "English",
series = "2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "346--350",
booktitle = "2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019",
address = "United States",
}
