Analysis method of massive intelligent electricity-consumption data based on improved k-means algorithm
A technology for smart electricity consumption and data analysis, applied in the field of smart grid, can solve the problems of large amount of calculation, low processing efficiency, unable to meet the needs of efficient mining of massive smart electricity consumption data, etc., achieve fast convergence speed, improve processing efficiency, shorten the time The effect of computing time
Inactive Publication Date: 2015-08-19
HANGZHOU TIANKUAN TECH
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Problems solved by technology
However, the traditional clustering analysis method has the bottleneck of large amount of calculation and low processing efficiency in the face of massive smart power consumption data, and cannot meet the efficient mining requirements of massive smart power consumption data.
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Embodiment
[0028] Embodiment: a kind of massive intelligent power consumption data analysis method based on improved k-means algorithm comprises the following steps:
[0029] (1) First, establish a Map-Reduce parallel processing model for household users, including the household user number, house area, number of family members, daily electricity consumption, peak and valley electricity, and the number of household appliances, including household electricity consumption information, and Using the Map-Reduce parallel processing model as the data object to create a massive intelligent power consumption data analysis framework;
[0030] Massive smart power consumption data analysis architecture such as figure 1 As shown, the master / slave architecture mode is adopted to realize the storage of massive intelligent power consumption data of power users, and the mining and analysis of potentially valuable information of massive intelligent power consumption data is realized based on the parallel...
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The present invention relates to an analysis method of massive intelligent electricity-consumption data based on an improved k-means algorithm. The analysis method comprises: firstly, establishing a Map-Reduce parallel processing model of the data of electricity-consumption information of residential consumers, such as the number of residential consumers, house areas, the number of family members, daily electricity consumption, the peak of electricity consumption, and the number of household electrical appliances; using the k-means algorithm to comprehensively consider two factors of the selection of an initial clustering center and the selection of the number of clusters, to improve the improved k-means algorithm, wherein the density of data objects is used as a selection standard of the initial clustering center, and a distance between clusters and the dispersing degree of objects in the clusters are used as the important references to select the number of clusters; optimizing the initial clustering center under the Map-Reduce parallel processing model to accurately position the clustering center; performing parallel mining on the data which belongs to each cluster to complete the analysis of electricity-consumption data. Through experiments on Hadoop clusters, results prove that the method provided by the present invention is stable, efficient and feasible to operate, and has a high speed-up ratio.
Description
technical field [0001] The invention relates to the technical field of smart grids, in particular to a method for analyzing massive smart power consumption data based on an improved k-means algorithm. Background technique [0002] In recent years, in the face of the strong growth of power demand and increasingly tight power supply in the field of smart power consumption, as well as the ever-increasing task of energy conservation and emission reduction, the smart grid with the basic technical characteristics of informatization, automation and interaction has become a worldwide Research hotspots. In my country, the coverage of electricity consumption information collection by power supply enterprises has gradually expanded from covering only important special line users to various power sites including various special line users, general industrial and commercial households, low-voltage residents, etc. The scale of terminals and meters has also increased accordingly, and the a...
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Login to View More IPC IPC(8): G06F17/30
CPCG06F16/35Y02D10/00
Inventor 周天和卢晓飞张元元蔡荣
Owner HANGZHOU TIANKUAN TECH