A method for identifying abnormalities in environmental protection management and control by decomposing enterprise electricity consumption data based on time series

Abstract

The present invention provides a method for identifying abnormalities in environmental management and control by decomposing enterprise electricity consumption data based on time series, including S1, constructing a distribution frequency table of electricity consumption data, including obtaining enterprise electricity consumption data Q and calculating Q max , mean Q ave , Q min ; Based on the time series decomposition method, the enterprise electricity consumption data Q is decomposed into trend items, cycle items, and residual item R, and the R of the residual item R is calculated max , mean R ave , R min ; Construct the environmental protection management and control implementation frequency P to the enterprise's electricity consumption data Q and the residual item R distribution frequency table of environmental protection management and control implementation; S2, count the historical electricity consumption data of several enterprises, and calculate the environmental protection management and control implementation frequency P; S3, based on environmental protection management and control Implement the frequency P to judge whether an enterprise is an enterprise with abnormal environmental protection control. The method of the present invention can finely analyze the change law of electricity consumption data caused by actions such as environmental protection emergency response or other management and control measures, and accurately judge whether the enterprise implements the emergency response according to the requirements.

Description

technical field [0001] The invention relates to the technical field of smart grids, in particular to a method for identifying abnormalities in environmental protection management and control by decomposing power consumption data of enterprises based on time series. Background technique [0002] In recent years, my country's air pollution control has achieved remarkable results, and the ambient air quality has improved year by year. At present, for the detection of pollutants such as PM2.5, on the basis of automatic monitoring of pollutant emission concentrations, it also integrates video surveillance, water and electricity consumption for environmental protection facilities. Monitoring and other Internet of Things supervision means, realize early warning management through big data analysis, and improve the ability to discover intelligent problems. Among them, industrial electricity consumption data has the characteristics of wide coverage and strong real-time accuracy, which ...

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.

Images