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Ai4Energy: Heading Towards a New Era of Smart Energy

We are dedicated to developing applications of artificial intelligence in the energy sector, including various areas such as differential algebraic equations (DAEs), operations research (OR), and machine learning (deep learning, reinforcement learning).

Our current project focus primarily revolves around modeling, simulation, optimization, and control of energy systems (DAEs and OR). This is aimed at providing fundamental software support for optimizing the design and operation of energy systems. In the realm of machine learning, we mainly explore the application of novel methods. Our projects are built upon the robust Juliaopen in new window ecosystem. Regarding optimization and design issues, we primarily develop models and solutions for comprehensive energy system optimization. For optimization during operation, we engage in the development of simulation engines, parameter identification, and model predictive control.

We also offer related graduate courses. Please visit Smart Energy: From Concept to Practiceopen in new window for more information.


  • Energy System Simulation Engine: Our Ai4EMetaPSEopen in new window is a full-process simulation engine that utilizes equation-based object-oriented modeling based on differential algebraic equations (DAEs) to model energy system components. It is used for steady-state and dynamic simulation of energy systems, handling both continuous-time and discrete-event problems.

  • Energy System Optimization Engine: We employ a componentized modeling approach based on hierarchy to handle energy system optimization and design problems using packages like JuMP.jlopen in new window.

  • Energy System Model Predictive Control: Based on our simulation engine, we use the optimization engine for parameter identification (model calibration) and develop software for model predictive control of energy systems. We have developed an optimal control package, OptControlopen in new window. You can find relevant documentation hereopen in new window.

  • Renewable Energy Component Library: Our renewable energy libraryopen in new window is suitable for comprehensive energy system simulation and is continually updated.

  • Comprehensive Energy System Virtual Simulation Laboratory: Our comprehensive energy system virtual simulation laboratoryopen in new window is designed for comprehensive energy system simulation and is continually updated.

  • Application of Machine Learning Methods.

  • Smart Energy Management Information System.