A new method, based on regularity analysis of cellular load, is presented to determine the reasonable maximum of the load density index method of spatial load forecasting (SLF). Through analysis of cellular historical load data, each cellular load is decomposed into regular and random components. The regular part is used to reveal the regularity of the cellular load in different cycles, using random components to describe and characterize the intrinsic error of the cellular load. Adverse effects that occur by eliminating random components to suppress the intrinsic error allow extraction of the maximum of the regular part as the Class I cellular reasonable load maximum. Using the reasonable maximum and the land use information of Class I cells, load density of I is obtained. The load density is used to determine the coordination coefficient of the classification load density. According to the constraint relationship of the historical load, we established the power supply area, land information, classification load density, relationship equation between Class I cellular load, and classification load density. Classification load density index is obtained by using the least square method and is used to predict its value in the target year. Then the load of Class II cell can be obtained, so that the spatial load forecasting of the urban power system is realized. The analysis of an example indicates that the method is effective in improving the prediction accuracy of spatial load forecasting.