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Efficient regional forecasting is a critical task for system operators and utilities that manage the generation of various wind farms spread over a region. This paper proposes an aggregate prediction method based on the search for similarities between the current wind speed predictions in a set of locations, and historical wind speed predictions. The aggregate power prediction is constructed from the measures of aggregate power generated during moments from a historical data set in which the wind speed predictions were similar to the current ones, using smoothing techniques. The methodology is applied to the hourly wind power forecast for the Spanish peninsular system, and compared with the predictions obtained with SIPREOLICO, the wind power prediction tool used by the Spanish system operator, and also with the aggregate predictions provided by another forecasting agency. The proposed methodology shows considerably smaller prediction errors than the competitors.