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With the encouragement of new energy policies and the continuous promotion of solar energy development. Distributed photovoltaic (PV) grid connection operation has become a trend. However, due to the large impact of the weather, the power output of distributed PV is unstable. It is difficult for the grid scheduling department to timely adjust the power generation and load of the grid based on PV power changes, resulting in difficulty in balancing supply and demand, affecting the stable operation of the grid and PV consumption. In response to the above issues, this paper proposes a wavelet neural network-based distributed PV grid-connected power prediction method, which provides data reference for the scheduling department through the prediction of distributed PV power. First, distributed PV power prediction architecture is studied, including the sensing layer, prediction layer, and service layer. Then the specific functions of the prediction layer are designed based on a wavelet neural network. Finally, the simulation results show that the proposed method can effectively achieve the power prediction of distributed PV.
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