The 2nd International Workshop on Big Data Analysis for Smart Energy
(BigData4SmartEnergy 2019)


The 2nd International Workshop on Big Data Analysis for Smart Energy (BigData4SmartEnergy 2019)

February 27, 2019, Kyoto, Japan

In conjunction with the IEEE BigComp 2019 - 6th IEEE International Conference on Big Data and Smart Computing

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.


Paper submission due
December 3, 2018 January 18, 2019
Acceptance notification
December 21, 2018 January 23, 2019
Camera-ready copies due
December 28, 2018 January 31, 2019
Author registration due
December 28, 2018 January 25, 2019
Workshop date
February 27, 2019


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
  • 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
  • Seyoung Yun, KAIST, Korea


08:50-09:30, February 27 (Wednesday), 2019


09:30-09:40, February 27 (Wednesday), 2019

Opening Address (Ho-Jin Choi, KAIST, Korea)

09:40-10:40, February 27 (Wednesday), 2019

Session 1

Surveillance Video-based Warning System of Extreme Rainfall for Power Grid
Zhun Li, Jonghwan Hyeon and Ho-Jin Choi (KAIST, Korea)
Generative Adversarial Tic-tac-toe Generation for Explainable Game Generation
Dongkun Lee and Ho-Jin Choi (KAIST, Korea)
Gradient Compression with Random Projection
Sangmook Kim and Se-Young Yun (KAIST, Korea)

10:40-11:00, February 27 (Wednesday), 2019

Coffee Break

11:00-12:00, February 27 (Wednesday), 2019

Keynote Speech

AI for Smary Energy
Hyeonkyu Lee and Ho-Jin Choi (KAIST, Korea)

12:00-14:00, February 27 (Wednesday), 2019

Lunch Break

14:00-16:00, February 27 (Wednesday), 2019

Session 2

Multilayer Perceptron-based Ensemble Model for Short-term Electric Load Forecasting
Seungwon Jung, Minjae Son, Jihoon Moon and Eenjun Hwang (Korea University, Korea)
Optimizing Hyper-parameters of Artificial Neural Network for Short-Term Load Forecasting
Sungwoo Park, Jihoon Moon and Eenjun Hwang (Korea University, Korea)
Building Energy Consumption Forecasting: Enhanced Deep Learning Approach
Jonghwan Hyeon, Hyeyoung Lee, Bowon Ko and Ho-Jin Choi (KAIST, Korea)
Comparison of Simulation Tools for the Future Distribution System
Gyul Lee, Seonhyeog Kim and Yong-June Shin (Yonsei University, Korea)
Detecting Spammers on Social Networks Using Strongly Connected Components in the Distributed Environment
Heesang Kim, Suan Lee, Sungjin Park and Jinho Kim (Kangwon National University, Korea)
Query Workload-aware RDF Data Partitioning Using Clustering Algorithm in Smart Grid
Dong-Jae Lee, Ina Kim and Kyu-Chul Lee (Chungnam National University, Korea)

16:00-16:20, February 27 (Wednesday), 2019

Coffee Break

16:20-18:00, February 27 (Wednesday), 2019

Session 3

An Automatic Collection System for Open Data in the Public Sector
Juhong Namgung, Myeong-Seon Gil and Yang-Sae Moon (Kangwon National University, Korea)
Encryption Broker Service for Data Center in Electric Power Infrastructure
Dongseop Lee, Hyunjin Kim and Jaecheol Ryou (Chungnam National University, Korea)
False Building Type Detection by Energy Usage Analysis
Minsu Kwon, Zhun Li and Ho-Jin Choi (KAIST, Korea)
A Study of Privacy Protection Researches for Location-based Queries in LBS
Chris Soo-Hyun Eom and Wookey Lee (Inha University, Korea)
Restoration of Lost Context for Language Modeling
Chan-Yong Park, Chae-Gyun Lim, Young-Jun Lee and Ho-Jin Choi (KAIST, Korea)

18:00-18:10, February 27 (Wednesday), 2019

Closing Remarks (Ho-Jin Choi, KAIST, Korea)


For any inquiries, please contact: