Method for recognizing environmental protection control abnormity by decomposing enterprise power consumption data based on time sequence

Abstract

The invention provides a method for recognizing environmental protection control abnormity by decomposing enterprise power consumption data based on a time sequence. The method comprises the following steps: S1, constructing a distribution frequency table of the power consumption data: acquiring enterprise power consumption data Q, and calculating Qmax, a mean value Qave and Qmin; decomposing enterprise power consumption data Q into a trend term, a periodic term and a residual term R based on a time sequence decomposition method, and calculating Rmax, a mean value Rave and Rmin of the residual term R; constructing an environmental protection management and control implementation distribution frequency table of the environmental protection management and control implementation frequency P for the enterprise power consumption data Q and the residual item R; S2, counting historical power consumption data of a plurality of enterprises, and calculating an environmental protection management and control implementation frequency P; and S3, based on the environmental protection management and control implementation frequency P, determining whether a certain enterprise is an abnormal environmental protection management and control enterprise. According to the method, the change rule of power utilization data caused by behaviors such as environmental protection emergency response or other management and control measures can be finely analyzed, and whether an enterprise implements emergency response according to requirements or not is accurately judged.

Description

Technical field [0001] The present invention relates to the technical field of intelligent grids, in particular a method based on timing decomposition enterprise electrical data to identify environmentally friendly controlled abnormalities. Background technique [0002] In recent years, my country's atmospheric pollution governance has achieved remarkable results, and the environmental air quality has been improved year by year. At present, the detection of pollutants such as PM2.5 has been combined on the basis of automatic monitoring of pollutant emission concentration, and the video surveillance, environmental protection facilities, water, electricity Surveillance and other Internet supervision methods, through large data analysis, early warning management, enhance intelligent problem discovery capabilities, of which industrial electricity data has the characteristics of high coverage, high and accurate accuracy, and operations with industrial enterprises production and pollut...

AI Technical Summary

Problems solved by technology

The above two methods are mainly based on qualitative judgment, and there is a problem that the analysis results of the enterprise's electricity consumption data are not accurate, and it is impossible to accurately judge whether the enterprise has implemented emergency response or other control measures.

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