The development of distributed renewable energy

such as photovoltaic power and wind power generation

makes the energy system cleaner

and is of great significance in reducing carbon emissions. However

weather can affect distributed renewable energy power generation

and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional

nonlinear dynamics of large-scale complex systems

and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning

the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.