Open AccessResearch on the correlation between electricity consumption and pollutant emission concentration based on DCCA and the prediction of pollutant emission concentration
Author(s) -
Guangye Li,
Jiaxin Zhang,
Xin jie Wen,
Langming Xu,
Ying Yuan
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/769/2/022065
Subject(s) - pollutant , electricity , environmental science , consumption (sociology) , environmental engineering , environmental economics , engineering , economics , chemistry , social science , organic chemistry , sociology , electrical engineering
Based on the big data of electricity consumption in industrial enterprises, this paper uses the DCCA (Detrended cross-correlation analysis) method to study the correlation between electricity consumption and pollutant emission concentration. The BP neural network method is also used to model and predict the pollutant emission trends, in order to monitoring of pollutant emissions in industrial enterprises reasonably. The research results show that there is an obvious correlation between electricity consumption data and pollutant concentration data, and the pollutant emission concentration can be predicted to a certain extent by building a BP neural network model. and the pollutant emission concentration can be predicted to a certain extent by building a BP neural network model. The prediction of pollutants through electricity consumption data helps enterprises to actively respond to the national environmental protection policy and promote national economic development, while allowing more enterprises to enjoy the convenience brought by big data analysis.