Day-ahead load forecasting is an important task for the power dispatching center to formulate dispatching plans. It is of great significance for formulating reasonable dispatching plans and ensuring the safety and reliability of power system operation. The time series of power loads experience random errors. The structures of intelligent algorithm based prediction models are complex, and calculation loads are heavy to fully extract load information. To solve this problem, this paper proposes a day-ahead power load forecasting method based on repeated wavelet transform-support vector machine（RWT-SVM） by using the historical power load time series of distributed power grids. The method uses WT to decompose the power load time series of distributed power grids into multiple subsequences; the MAE is applied to calculate the prediction error contribution of each subsequence; further decomposes the sequence with the largest MAE to improve the prediction ability of the model. The simulation results show that the forecasting method based on RWT-SVM is more accurate than other methods.