The wind power prediction for wind farm clusters is important for optimal scheduling of regional wind farms. The existing cluster prediction algorithms and cluster division methods do not consider the differential fluctuations among stations’ numerical weather prediction（NWP） along time scale

and the cluster isn’t divided reasonably. Consequently

we put forward a short-term wind power prediction method based on weather-process-dynamic wind farm cluster division. First

the 96-hour-time-scale prediction sample is equally divided into 16 sub-samples

followed by separate cluster division judging for each sub-sample. Then

the training set is organized for each sub-sample’s sub-cluster according to cluster division result. Finally

the bidirectional long-short term memory（BLSTM） neural network is used for power prediction for each sub-cluster. The results show that

using the proposed method

the prediction accuracy can increase by 1.69%

0.77%

and 0.59% compared to using the statistical upscaling method in 4 h-urtra-short-term

24 h-day-ahead

and 96 h-short-term wind power prediction

respectively. The research can provide a reference for the topic of wind farm cluster division and short-term power prediction.