Guest Editors
Prof. Dr. Wenlong Fu
Email: ctgu_fuwenlong@126.com
Affiliation: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002, China
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Research Interests: integrated modeling, simulation and control of renewable energy generation system, new energy power generation forecast, artificial intelligence application
Prof. Dr. Yunning Zhang
Email: yunningzhang@gmail.com
Affiliation: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002, China
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Research Interests: artificial intelligence application, application of intelligent systems and control theory in energy networks
Summary
Energy security and the efficient integration of renewable energy sources, particularly variable renewables like wind, solar, and hydropower, have become critical global priorities. The inherent intermittency and stochastic nature of these resources, coupled with fluctuating demand, pose significant challenges to grid stability. This necessitates sophisticated energy management strategies where accurate forecasting is paramount. Artificial intelligence and machine learning have emerged as powerful tools to develop high-precision forecasts for wind power generation, solar irradiance, and hydrological conditions for hydropower. Furthermore, the proliferation of smart sensors and the Internet of Things enables the collection of massive, high-resolution datasets. The central challenge now evolves from data collection to leveraging this information through advanced AI algorithms to optimize dispatch, enhance grid reliability, and solve pressing practical problems in the energy sector.
On this background, this Research Topic aims to compile cutting-edge research on Artificial intelligence and machine learning driven forecasting techniques for integrated wind-solar-hydro energy systems.
Topics of interest include, but are not limited to, the following:
· Data processing strategies for complex energy prediction
· Forecasting technologies for energy systems based on composite models
· Wind/ Photovoltaic/Hydro power forecasting
· Prediction integrating meteorological data
· Prediction of the operating status of power generation equipment
· Probability/interval prediction for energy systems
· Energy prediction based on intelligent algorithms
· Multi-objective optimization for energy systems forecasting
· Load forecasting based on artificial intelligence technologies
· Prediction technology based on error correction
Keywords
power forecasting, machine learning, artificial intelligence, renewable energy, intelligent algorithm, composite model
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