IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
21-23 October 2019 // Beijing, China

WS-4: AI in Energy Systems

WS-4: AI in Energy Systems

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Aim and Scope

Recent years have witnessed the rapid growth of Artificial Intelligence (AI) techniques, with applications ranging from computer vision, natural language processing, recommendations, to autonomous driving, decision making, and problem solving.  AI techniques have dramatically changed modern industrial systems such as energy systems and transportation systems. For smart energy systems, AI technology provides intelligent and effective tools for electricity generation, transmission, delivery, consumption, marketing management, and emergency response. For example, AI has been applied to produce ultra-accurate forecasts making it feasible to integrate demand side management and much more renewable energy into the power grids. Various machine learning-based security assessment tools have been designed for power grids to detect faults and attacks. Additionally, computer vision techniques have been exploited for remote power monitoring and control. Therefore, although AI is in its early stages of implementation, it is poised to revolutionize the way we produce, transmit, and consume energy.

The goal of this workshop is to solicit high-quality research articles proposing the state-of-the-art AI-based solutions to improve the stability, efficiency, security and resilience of the energy systems. Prospective authors are invited to submit manuscripts for possible publications in this special topic. Original research as well as high-quality review articles are all welcome.


  • Learning techniques in energy systems
    • Intelligent estimation and classification techniques
    • Deep learning in energy systems
    • Reinforcement learning in energy systems
    • Other learning techniques: fuzzy systems, decision trees, clustering
  • Multimedia and learning
    • Visualization for energy systems
    • Semantic techniques applied in energy systems
    • NLP and computer vision for energy systems
  • Distributed computing and control
    • Distributed computing in energy systems
    • Distributed control
    • Game theory and energy systems, mechanism design
  • Prediction and decision making
    • Loads and renewables forecasting and estimation
    • Condition monitoring and asset management
    • Real-time data analysis and decision making
  • Intelligent systems
    • Intelligent enterprise systems, e.g. intelligent outage management
    • Intelligent wide area monitoring, protection, control, and management
    • Innovative and/or emerging applications of intelligent systems in power systems
    • Knowledge-based systems including rule-based systems, expert systems, model-based reasoning
    • Hybrid intelligent systems in energy systems
    • Computational neuroscience in energy system
    • Molecular and quantum computing in energy systems
  • Privacy and security based on AI
    • Privacy-preserving analytics for energy systems
    • Fault diagnosis and prognosis
    • Cyber security analytics in energy systems