The invention proposes a short-period load prediction method for a microgrid based on SPSS and RKELM, and the method comprises the steps: (1), carrying out the online data collection, and periodicallyupdating a historical database; (2), carrying out the preprocessing of historical data, and extracting load sample features; (3), constructing an offline load prediction model; (4), screening a historical sample similar to a to-be-predicted point precursor load as an online training sample through SRC (Spearman Rank Correlation); (5), calculating a load prediction value at a future moment according to the online training sample and the offline load prediction model. The method employs a rapid RKELM (Reduced Kernel Extreme Learning Machine), a chaos particle swarm optimization algorithm and the SRC, and achieves the building of a prediction model comprising offline parameter optimization and an online load. Through the periodic updating of model parameters, the method guarantees the timeliness of an algorithm, reduces the complexity of online prediction and calculation, reduces the storage quantity of historical data, reduces the calculation cost, and can achieve the more accurate prediction of the short-period and super-short-period loads of the microgrid.