The 3rd International Workshop on Big Data Analysis for Smart Energy
(BigData4SmartEnergy 2020)

CALL FOR PAPERS

The 3rd International Workshop on Big Data Analysis for Smart Energy (BigData4SmartEnergy 2020)

February 19, 2020, BEXCO, Busan, Korea

In conjunction with the IEEE BigComp 2020 - 7th 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

ORGANIZING COMMITTEE

General Chair

  • Ho-Jin Choi, KAIST, Korea

Organizing Chairs

  • Chan-Hyun Youn, KAIST, Korea
  • Kyuchul Lee, Chungnam National University, Korea
  • Yang-Sae Moon, 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
  • Jinho Kim, 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

PROGRAM

09:00-12:30, February 19 (Wednesday), 2020

The Symposium of KEPCO AI Cluster (Location: Room #325)

09:00 - 09:15
Opening Remark
Ho-Jin Choi
09:15 - 09:45
An eXplainable AI (XAI) Approach to Image Captioning with Domain-specific Ontology
Seung-Ho Han
09:45 - 10:15
An Efficient Online Deep Learning for Accelerated Stream Data Processing
Seong-Hwan Kim
10:15 - 10:45
Multiple SPARQL Query Processing with MapReduce
InA Kim
10:45 - 11:00
Coffee Break
11:00 - 11:30
Stochastic Distributed Data Stream Partitioning using Task Locality
Siwoon Son
11:30 - 12:00
Solving Cold Start Problem in Load Forecasting
Sungwoo Park
12:00 - 12:30
Reliable Relation Path Representation Learning for Knowledge Graph Completion
Seungmin Seo

10:40-12:25, February 19 (Wednesday), 2020

Paper Presentation (Location: Room #326)

10:40 - 10:45
Opening Remark
Ho-Jin Choi
10:45 - 11:00
Domain-Specific Image Caption Generator with Semantic Ontology
Seung-Ho Han and Ho-Jin Choi
11:00 - 11:15
Accelerating Randomly Projected Gradient with Variance Reduction
Seongyoon Kim and Seyoung Yun
11:15 - 11:30
Deep Electric Pole Anomaly Detection and Unsupervised Description Generation
Dongkun Lee and Ho-Jin Choi
11:30 - 11:40
Coffee Break
11:40 - 11:55
Poisoning Attack on Show and Tell Model and Defense Using Autoencoder in Electric Factory
Dongseop Lee, Hyunjin Kim, and Jaecheol Ryou
11:55 - 12:10
Forecasting Building Electricity Power Consumption Using Deep Learning Approach
Young-Jun Lee and Ho-Jin Choi
12:10 - 12:25
Deep Learning-Based Analysis on Monthly Household Consumption for Different Electricity Contracts
Chae-Gyun Lim and Ho-Jin Choi

CONTACT

For any inquiries, please contact: