Resrarch On Prediction Of Spatial Load Density In Power System

Space power system Load Forecasting(Spatial Load Forecasting,SLF)is based on the urban planning in the space and time to the city power level of a prediction.With the gradual increase of urbanization level,the demand for space power load forecasting is increasing year by year.Effective spatial load forecasting of power system can provide favorable support for urban power grid planning and construction,and provide constructive suggestions for new power plants and power stations,which is one of the indispensable factors affecting urban development.Among them,the level of urban electricity consumption is shown in the form of power load,that is,the sum of the electric power that the electric power equipment of the electric power users takes from the electric power system at a certain time.However,most of the traditional power load forecasting focuses on the prediction of load size at a certain time or period,ignoring the spatial analysis of power load size.Spatial load forecasting of power system includes not only time information,but also space information of the area to be measured.The forecasting result can reflect the power load of the area to be measured at a certain time or period.The total amount of power load based on time series is located in a specific space coordinate,and the power load is assigned to the size of the power load.Geographic information and spatial load of power system are generally predicted according to the classification of urban land use,and its reasonable measurement has a certain guiding role in urban future planning.In the process of power system spatial load forecasting,it is necessary to consider the classification of land use types and how to reasonably correlate the geographic information with the size of power load.Therefore,this paper borrows Geographic Information System(GIS)as a tool for carrying and displaying power load and spatial data,and uses it to process and store the number of space.According to the advantages,the power load of the tested area is visually demonstrated from the spatial perspective.The new load density index method is adopted to balance the uneven distribution of similar loads in different land types by the load density coordination coefficient.Firstly,the paper and classification of spatial load forecasting,the principle of load forecasting and related factors are analyzed theoretically.Because it is necessary tocorrelate the power load information with the location information in the process of power system spatial load forecasting,this paper briefly introduces the data characteristics and functions of geographical information system(GIS).Secondly,in the existing spatial load forecasting methods,the problems of non-uniform load distribution or insufficient mining of measured historical data are often ignored.Therefore,a dual cell load forecasting model is proposed in this paper.And then,data mining technology is used to preprocess historical data and K-means clustering analysis technology is used to determine the land use information classification which affects the spatial load density of power system.K-means clustering analysis is used to identify land use information classification affecting the spatial load density of power system.In the process of clustering analysis,the improved K-means algorithm is used to optimize the original algorithm.Selecting initial clustering centers based on the density of weighted Euclidean distance.In the process of running the algorithm,the algorithm adjusts itself according to the importance of the attributes of the data set,thus reducing the number of iterations and improving the accuracy of the results.Finally,this paper puts forward the model of classification and clustering analysis of land use types,to fully tap the historical data and calculation,based on a certain city engineering examples of an administrative region as the research object,carries on the spatial load forecasting,and error analysis,comparison results show that the prediction accuracy of this method is higher,show that the method is desirable.