Intelligent building microgrid power utilization behavior identification method

Abstract

The invention discloses an intelligent building microgrid power utilization behavior identification method. The intelligent building microgrid power utilization behavior identification method is characterized in that power utilization big data is obtained through an intelligent building microgrid power utilization data acquisition terminal and monitoring of each environment sensor; then the obtained data is subjected to pretreatments (data integration, data filling and feature normalization); the load feature parameter of each powered device is extracted from a pretreated data sample by means of a kernel principal component analysis method; the environment factor of each power utilization behavior feature of the user and the contribution rates of different powered devices, i.e., the feature weight of each influence factor of fuzzy C-mean value clustering are determined by means of a multi-variable multi-scale sample entropy weight method; finally the obtained feature data set is subjected to cluster analysis through the fuzzy C-mean value clustering method and the user power utilization behavior identification accuracy and rapidity can be improved. According to the invention, the building microgrid user power utilization behavior can be effectively identified, and a basis and reference can be provided for scheduling of the building microgrid power utilization load by the power grid, and furthermore the fact that the user saves power in life, and power wasting behaviors are reduced is promoted.

Description

technical field [0001] The invention belongs to the technical field of power consumption of smart building microgrids, and relates to a method for identifying power consumption behaviors of smart building microgrids. Background technique [0002] With the continuous construction and development of the smart grid, user electricity consumption data has gradually accumulated into big data. Although these large electricity consumption data seem to be very chaotic, they hide the user's electricity consumption behavior, and there are certain gaps between the data. connect. Mining scattered massive data and identifying the types of user electricity consumption behavior can help the power grid understand the user's personalized and differentiated service needs, and provide a basis and reference for the power grid to schedule the power load of the smart building microgrid, thereby improving residents' lives. Save electricity and reduce electricity consumption. [0003] At present, ...

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Problems solved by technology

[0003] At present, some researchers decompose the user's electricity load into basic load and seasonal load, and use the adaptive fuzzy C-means clustering algorithm to cluster the user's basic load and seasonal load. However, in practical applications, some users The total load of electricity consumption is the same, but the basic load and the equipment used are also different, and the user's electricity consumption behavior cannot be identified and classified with high precision; in addition, other scholars use the entropy weight method to calculate the entropy and weight of the user's electricity consumption characteristic information, and then in Cluster analysis is performed on users by clustering algorithm, but this method cannot calculate the probability of users' electricity consumption behavior as a whole, and there is a deficiency in judging the accuracy of users' electricity consumption behavior

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