The invention discloses a photovoltaic power generation power prediction method based on support vector machine regression, and the method comprises the steps: firstly, obtaining the historical outputdata and numerical weather forecast data of a target station; Screening out meteorological factors with high correlation from the meteorological factors; Secondly, preprocessing the historical data set, selecting appropriate input parameters, and performing data normalization to construct an input vector of a support vector machine; Calculating correlation degrees between the historical data setand four typical days day by day by using a grey correlation coefficient method; Clustering correlation degree calculation results so as to divide the historical data into four training sets accordingto weather types; Carrying out training modeling on the classified historical samples by adopting a support vector machine regression algorithm to obtain a prediction model; Determining the weather type of the to-be-predicted day through correlation calculation, and calling a corresponding prediction model; And finally, prediction day value weather forecast parameters are input, and a power prediction result is obtained based on a support vector machine regression algorithm and a prediction model.