In view of the load density method based on intelligent algorithm heavily dependent on the sample and hardly applicable

a spatial load clustering and integrated forecasting method of distribution network considering regional difference is proposed. Firstly

power users and regional information on different cities and different types are obtained through massive investigation. Secondly

according to an analysis of the inadequacy of the current load classification mode

a method based on typical daily load curves is put forward to check the sample load classification label and select the proper samples. Thirdly

a full sample space is constructed by the selected samples

which is further classified into a hierarchical sub-sample space according to a two-level partition of regional property and checked classification label. Finally

a support vector machine(SVM)is built with the most similar sub-sample space to the plot to be predicted

thus the spatial load distribution is obtained. An actual case shows the practicality and effectiveness of the proposed method

which meets the accuracy requirements.