A Method of Electricity Utilization Load Analysis and Visualization Based on Data Mining of Electric Power Big Data

Qian Wang, Qian Sun, Qiang Li, Bo Zhu, Yu Liu, Tingjun Yang, Qi Wang

Abstract


Regional electricity utilization data is closely related to electrical load flow in terms of demand. The corresponding relation between electric power data and regional electricity load can be deeply analyzed through data mining technology in many aspects. Based on variation function theory and its structural analysis, the author realizes the unbiased optimal estimation on regional variations in finite regions through spatial local difference algorithm. According to the data of a number of known sample points in finite regions, a linear unbiased optimal estimation is carried out on unknown sample points after such things are taken into account as shape size and dimensional orientation of sample points, spatial position relation with unknown sample points and structural information provided by variation functions. The objective is to analyze real-time data of power operation with the combination of geographic information and to conclude the overall tendency of power demand so as to conclude the power demand tendency and social activity changes of an entire region and establish a demand model based on electric power data mining. The model is displayed through a new method of visualization based on authentic electric power data and GIS information.

Keywords


data mining; load analysis; electric power data; visualization; big data


DOI
10.12783/dtetr/emme2016/9782

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