Microgrid’s Performance Modeling & Optimization Method Based on Data Mining & Artificial Intelligence

With the maturity of renewable energy technology in recent years, micro-network has become an ideal power supply solution to the remote villages and islands. Recently, researchers have tried to reduce the cost of the system based on ideal assumptions. However, the factors that actually affect the system life cycle cost are varied. Including the control of the system, the maintenance mode of the system, the geographical factors of the power station and the configuration of the system will greatly affect the cycle cost of the whole system. This project attempts to find all the factors that affect the cost from a large number of raw data. The use of artificial intelligence technology will provide users with more accurate optimization program or to provide advice on its maintenance, so as to maximize the micro-grid power supply efficiency and reduce the system’s life cycle costs.

Faculty Supervisor:

William Dunford


Xiaotong Wang


Schneider Electric of Canada


Engineering - computer / electrical


Alternative energy




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