Abstract: In order to monitor and dispatch the large-scale wind farms and photovoltaic stations for power grid, it is needed to predict the regional power of new energy. A regional power prediction algorithm is proposed based on sub-region partition and statistical upscaling. According to the historical data of 9 wind farms and 16 photovoltaic stations in Zhejiang province from April to September 2016, six different combination schemes are compared. The mutual information theory (MI) and the minimal redundancy maximal relevance principle (mRMR) are used to accumulatively (mRMR-A) select four representative stations or directly (mRMR-D) select nine representative stations. The weight of each representative site is trained by cuckoo search algorithm, and the regional power is determined by scaling up. It is found that the MI-mRMR-A-CS-4 and MI-mRMR-D-CS-9 algorithms have low prediction errors with their monthly root mean square error being 8.51% and 7.64% respectively. It is concluded that when the summer monsoon is prevalent in Zhejiang region, the representative stations are selected based on mRMR principle with MI used as the index, and then the cuckoo search algorithm is used to obtain the power prediction values of sub-regions, and the cumulative power prediction results of all sub-regions are the best for the final regional power prediction results.