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In order to improve the planning level of distribution network and realize the rational planning and transformation of distribution network, the quality and efficiency of power supply can be improved effectively. A new load forecasting model is proposed for the distribution network load forecasting. In this algorithm, the support vector machine (SVM) is introduced into the load forecasting model based on grey relational analysis. The grey correlation analysis method is used to select the more suitable samples and train them. At the same time, the chaotic particle swarm optimization (PSO) is introduced to optimize the model to improve the accuracy of the algorithm. For example through the analysis of actual data on the performance of the algorithm, based on the results of the analysis show that there are significant differences in the algorithm proposed and using this method to predict the space distribution load precision. The method proposed can improve the distribution network load density prediction effective accuracy.