In order to make full use of the relationship between the cellular load and cellular properties to improve the results of spatial load forecasting (SLF)

a method of SLF based on multilevel clustering analysis and support vector machine (MCA-SVM) is proposed. Firstly

cells are generated and their properties set are established. The cells are classified by multilevel clustering analysis according to the properties

where k-means algorithm is used to determine the clustering number and the initial clustering centers. Secondly

the forecasting models based on support vector machine are developed for different cellular types. Meanwhile the genetic algorithm is used to optimize the parameters of the models for improving their fitness. Finally

the cellular properties are substituted into the models as input vectors to forecast the maximal load in the target year for all the cells

realizing spatial load forecasting. An actual SLF case shows the practicality and effectiveness of the proposed method. This work is supported by National Natural Science Foundation of China (No. 51177009) and Jilin Provincial Natural Science Foundation of China (No. 20140101079JC).