The First International Workshop on Big Data Analysis for Smart Energy
(BigData4SmartEnergy 2018)


The First International Workshop on Big Data Analysis for Smart Energy (BigData4SmartEnergy 2018)

January 15, 2018, Shanghai, China

In conjunction with the 2018 IEEE International Conference on Big Data and Smart Computing (IEEE BigComp2018)

Purpose and Scope

AI-based smart energy deals with the challenges of using big data and AI techniques for the efficient management of energy resources and facilities, the timely detection of energy problems and anomalies, and the effective prediction of energy demands and services in the future, hence has already become an important area of research in many countries. This workshop aims to create opportunities to share on-going works in smart energy industry and academia, to enhance collaboration between energy researchers and data scientists, and to foster new innovations of AI, big data, and smart computing technologies in such areas as smart energy platforms and infrastructure, monitoring and analysis of energy big data, customized demand forecast in smart cities, smart homes, and electric vehicles, new innovative energy services, and so on. Prospective authors are cordially invited to submit their original contributions covering completed or ongoing work in (but not limited to) the following areas:

  • Innovative AI techniques for future energy services and monitoring
  • Intelligent techniques for smart energy platforms and infrastructure
  • Big data analytics for smart energy operations, measurement and control
  • Real-time handling and storage of stream data for power facilities
  • Intelligent monitoring and real-time analysis of power energy big data
  • Data mining and metadata annotation of power energy big data
  • Big data- and/or block chain-based security systems for energy services
  • Evaluation, monitoring and visualization techniques for energy big data
  • Intelligent asset management of transmission and distribution facilities
  • Machine learning techniques for anomaly detection in power services
  • Electricity demand prediction for smart homes and smart cities
  • Smart grid and customized energy services demand prediction
  • Energy ecosystems and related issues


Prospective authors are invited to submit their papers, up to 4 pages in English and in PDF according to the IEEE two-column format for conference proceedings, through EasyChair using the link below. All submissions will be peer-reviewed by the Program Committees of the workshop.

Direct link for paper submission:

Paper templates:


Selected papers will be recommended to relevant SCIE journals after further revision and extension.

IMPORTANT DATES (all deadlines fixed)

Paper submission due
November 05, 2017
Acceptance notification
November 19, 2017 November 22, 2017
Camera-ready copies due
November 27, 2017
Author registration due
November 27, 2017
Workshop date
January 15, 2018


General Chair

  • Ho-Jin Choi, KAIST, Korea

Organizing Chairs

  • Chan-Hyun Youn, KAIST, Korea
  • Kyuchul Lee, Chungnam National University, Korea
  • Jinho Kim, Kangwon National University, Korea
  • Eenjun Hwang, Korea University, Korea
  • Sung-Bae Cho, Yonsei University, Korea

Program Committee

  • Hoon Choi, Chungnam National University, Korea
  • Jun Kyun Choi, KAIST, Korea
  • Mi-Jung Choi, Kangwon National University, Korea
  • Jaegul Choo, Korea University, Korea
  • Young-Seob Jeong, SoonChunHyang University, Korea
  • Pilsung Kang, Korea University, Korea
  • Myoung Ho Kim, KAIST, Korea
  • Chang-Ki Lee, Kangwon National University, Korea
  • Kyong-Ho Lee, Yonsei University, Korea
  • Tae-Eog Lee, KAIST, Korea
  • Yang-Sae Moon, Kangwon National University, Korea
  • Cheong Hee Park, Chungnam National University, Korea
  • Jaecheol Ryou, Chungnam National University, Korea
  • Seungwon Shin, KAIST, Korea
  • Yong-June Shin, Yonsei University, Korea
  • Turki Turki, King Abdulaziz University, Saudi Arabia
  • Yong-Ik Yoon, Sookmyung Womens University, Korea
  • Seyoung Yun, KAIST, Korea


Time-Synchronized Measurements and Applications for Monitoring of Intelligent Electric Power Systems


Yong-June Shin
Yong-June Shin
  • Professor, Yonsei University


For the future smart grid, time-synchronized measurements such as synchrophasor and advanced metering infrastructure (AMI) have been introduced into the existing power grid, which are featured by communication capabilities and higher measuring resolution contrary to the conventional measuring devices. These capabilities will enable us to monitor the grid in real-time manner for efficient and reliable operation of smart grid. In addition, these devices are considered as main sources to generate power system big data for intelligent smart energy services. This paper presents current status of time-synchronized measurements and their applications in big data. Moreover, applications and potentials in power system application are discussed when synchrophasor and AMI are time-synchronized across layers.


Yong-June Shin received his B.S. degree from the Department of Electrical Engineering, Yonsei University, Seoul, Korea, in 1996 with early completion honors and the M.S. degree from The University of Michigan, Ann Arbor, in 1997. He received the Ph.D. degree from the Department of Electrical and Computer Engineering, The University of Texas at Austin, in 2004. Upon his graduation, he joined the Department of Electrical Engineering, The University of South Carolina as an Assistant Professor. He was promoted to Associate Professor with tenure in 2011. He joined School of Electrical and Electronic Engineering, at Yonsei University, Seoul, Korea as Associate Professor in 2012. He is a recipient of the United States National Science Foundation CAREER award in year 2008, and General Electric Korean-American Education Commission Scholarship. Dr. Shins current research interests are characterized by the application of novel digital signal processing techniques to a wide variety of important transient and nonlinear problems in smart electric power grid.


08:50-09:00, January 15 (Monday), 2018

Opening (Ho-Jin Choi, KAIST)

09:00-10:00, January 15 (Monday), 2018

Session SE1: Big Data Techniques for Smart City (Chair: YongIk Yoon, Sookmyung Women’s University)

A Unsupervised Learning Method of Anomaly Detection Using GRU
Zhaowei Qu, Lun Su, Xiaoru Wang, Shuqiang Zheng (Beijing University of Posts and Telecommunications)
Towards Smart City Platform Intelligence: PI Decoupling Math Model for Temperature and Humidity Control
Aigerim Altayeva, Young Im Cho (Gachon University)
Towards Intelligent IoT Smart City Platform Based on OneM2M Guideline: Smart Grid Case Study
Aigerim Altayeva, Young Im Cho (Gachon University)
Fusion Sentimental Analysis in Self-Growth Broadcasting
Svetlana Kim, YongIk Yoon (Sookmyung Women’s University)

10:00-10:30, January 15 (Monday), 2018

Coffee Break

10:30-11:20, January 15 (Monday), 2018

Keynote Speech (Chair: Ho-Jin Choi, KAIST)

Time-Synchronized Measurements and Applications for Monitoring of Intelligent Electric Power Systems
Yong-June Shin (Yonsei University)

11:20-12:00, January 15 (Monday), 2018

Session SE2: Smart Energy Platform Techniques (Chair: Jin-Ho Kim, Kangwon National University)

Introduction to KEPCO Smart Energy Cluster
Ho-Jin Choi (KAIST)
Dynamics of Electricity Consumers for Classifying Power Consumption Data using PCA
Minkyung Kim, Sangdon Park, Kireem Han, Nakyoung Kim, Junkyun Choi (KAIST)

12:00-14:00, January 15 (Monday), 2018

Lunch Break

14:00-15:30, January 15 (Monday), 2018

Session SE3: Artificial Intelligence Techniques for Smart Energy (Chair: Se-Young Yun, KAIST)

Text to Game Character: Starting Point for Conditional Generative Adversarial Video Composition
Dongkun Lee, Ho-Jin Choi (KAIST)
Efficient Searching of Subhypergraph Isomorphism in Hypergraph Databases
Tae Wook Ha, Jung Hyuk Seo, Myoung Ho Kim (KAIST)
An Adaptive Batch-Orchestrating Algorithm for Distributed Deep Learning System in Heterogeneous GPU Cluster
Eunju Yang, Seong-Hwan Kim, Tae-Woo Kim, Minsu Jeon, Sangdon Park, Chan-Hyun Youn (KAIST)
Noisy Power Method with Grassmann Average
Se-Young Yun (KAIST)
Energy Operation System Using Uncertain Electric Data in Korean Tariff System
Jangkyum Kim, Nakyoung Kim (KAIST), Kangsan Kim (LG Electronics Inc.), Jaeseob Han (KAIST), Joohyung Lee (Gachon University), Junkyun Choi (KAIST)

15:30-16:00, January 15 (Monday), 2018

Coffee Break

16:00-17:00, January 15 (Monday), 2018

Session SE4: Big Data Management for Smart Energy (Chair: Han-Gyu Kim, KAIST)

Anomaly Pattern Detection on Data Streams
Cheong Hee Park (Chungnam National University)
Energy Peak Reduction Mechanism with Prediction of Demand and PV Generation on Big Data
Tai-Yeon Ku, Wan-Ki Park (ETRI), Hoon Choi (Chungnam National University)
The Architectural Design of Storage System for Power Data Management
Seung-Won Yoon, Ina Kim, Kyu-Chul Lee (Chungnam National University)
A Study on Utilization of Blockchain for Electricity Trading in Microgrid
Geunyoung Kim, Junhoo Park, Jaecheol Ryou (Chungnam National University)

17:00-18:00, January 15 (Monday), 2018

Session SE4: Data Modelling Techniques for Smart Energy (Chair: Yang-Sae Moon, Kangwon National University)

Knowledge Graph Modeling for Semantic Integration of Energy Services
Sejin Chun, Xiongnan Jin, Seungmin Seo, Kyong-Ho Lee (Yonsei University), Youngmee Shin, Ilwoo Lee (ETRI)
Intelligent Fault Detection via Dilated Convolutional Neural Networks
Mohammad Azam Khan (Korea University), Yong-Hwa Kim (Myongji University), Jaegul Choo (Korea University)
Locality Aware Traffic Distribution in Apache Storm for Energy Analytics Platform
Siwoon Son, Sanghun Lee, Myeong-Seon Gil, Mi-Jung Choi, Yang-Sae Moon (Kangwon National University)
Fault Tolerance for Software-Defined Networking in Smart Grid
Chanhee Lee, Seungwon Shin (KAIST)

18:00-18:10, January 15 (Monday), 2018

Closing (Ho-Jin Choi, KAIST)


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