Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity

Wei Guo; Xiang Zha; Kun Qian; Tao Chen

doi:10.1109/ICECE48499.2019.9058539

Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity

Wei Guo
, Xiang Zha
, Kun Qian
, Tao Chen

Thermal Power Research Institute
Nanjing NARI Integrated Energy Co. Ltd.
The University of Tokyo
Virginia Polytechnic Institute and State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	346-350
Number of pages	5
ISBN (Electronic)	9781728147840
DOIs	https://doi.org/10.1109/ICECE48499.2019.9058539
Publication status	Published - Dec 2019
Externally published	Yes
Event	2nd IEEE International Conference on Electronics and Communication Engineering, ICECE 2019 - Xi'an, China Duration: 9 Dec 2019 → 11 Dec 2019

Publication series

Name	2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019

Conference

Conference	2nd IEEE International Conference on Electronics and Communication Engineering, ICECE 2019
Country/Territory	China
City	Xi'an
Period	9/12/19 → 11/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Active learning
Machine Learning
Power Systems
Smart Grid
Weakly Supervised Learning

Access to Document

10.1109/ICECE48499.2019.9058539

Cite this

Guo, W., Zha, X., Qian, K., & Chen, T. (2019). Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity. In 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019 (pp. 346-350). Article 9058539 (2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECE48499.2019.9058539

Guo, Wei ; Zha, Xiang ; Qian, Kun et al. / Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity. 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 346-350 (2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019).

@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",

}

Guo, W, Zha, X, Qian, K & Chen, T 2019, Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity. in 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019., 9058539, 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 346-350, 2nd IEEE International Conference on Electronics and Communication Engineering, ICECE 2019, Xi'an, China, 9/12/19. https://doi.org/10.1109/ICECE48499.2019.9058539

Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity. / Guo, Wei; Zha, Xiang; Qian, Kun et al.
2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 346-350 9058539 (2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity

AU - Guo, Wei

AU - Zha, Xiang

AU - Qian, Kun

AU - Chen, Tao

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - Active learning

KW - Machine Learning

KW - Power Systems

KW - Smart Grid

KW - Weakly Supervised Learning

UR - https://www.scopus.com/pages/publications/85084073201

U2 - 10.1109/ICECE48499.2019.9058539

DO - 10.1109/ICECE48499.2019.9058539

M3 - Conference contribution

AN - SCOPUS:85084073201

T3 - 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019

SP - 346

EP - 350

BT - 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Electronics and Communication Engineering, ICECE 2019

Y2 - 9 December 2019 through 11 December 2019

ER -

Guo W, Zha X, Qian K, Chen T. Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity. In 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 346-350. 9058539. (2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019). doi: 10.1109/ICECE48499.2019.9058539

Can Active Learning Benefit the Smart Grid? A Perspective on Overcoming the Data Scarcity

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this