A study on the space distribution and temporal evolvement of electric power load

Spatial load forecasting hinges on choosing suitable load density. Because of load density is influenced by many uncertain factors, taking various objective factors into account on the basis of expert experience judgment will be a great help to improve the precision of forecasting load density. The selection of load density bases on a large number of samples. This paper establishes cloud generator to extend the data of finite samples. This paper selects the influence factors which play an important role in the load density and introduces cloud theory and analytic hierarchy process to analyze the combination weight vector of various factors. Use the ideal approximation method to select the optimal load density based on the expanded sample data and modify the load density based on the similarity method. Inputting the values of influencing factors in a given year of the area to be predicted, it can be worked out that the load density of the area in the given year. This method can not only predict the spatial distribution of the electric power load, but also predict the evolution process of the spatial load. An example demonstrates the superiority of the proposed model in spatial load forecasting.