The invention provides a combined 
wind power prediction method suitable for a distributed 
wind power plant. The method comprises the following steps: step 1, acquiring data and pre-
processing; step 2, utilizing a training sample set and a prediction sample set which are normalized to build a 
wind speed prediction model based on a 
radial basis function neural network and predict the 
wind speed and variation trend of distribution fans at the next moment; step 3, building a distributed 
wind power plant area CFD (computational fluid dynamics) model and externally deducing the prediction 
wind speed of each 
fan in the 
plant area according to factors such as the 
terrain, coarseness and wake current influence of a distributed 
wind field; step 4, acquiring the power data of an 
SCADA (
supervisory control and 
data acquisition) 
system fan of the distributed 
wind field; and step 5, adopting correlation coefficients. The invention firstly provides a double-layer combined neural network to respectively predict the wind speed and power. Models are respectively built through adopting appropriate efficient neural network types, and improved 
particle swarm optimization with ideas of 'improvement', 'variation' and '
elimination' is additionally added to optimize the neural network, so that the speed and precision of modeling can be effectively improved, and the decoupling between wind speed and power is realized.