Algorithm for Pollution Emission Monitoring and Warning in Enterprises Based on Symbolic Aggregate Approximation-Autoregressive Moving Average Model Hybrid Model
With the rapid development of society and the acceleration of industrialization,environmental pollution has become increasingly prominent,and strengthening environmental supervision and governance has become an urgent task.In order to effectively control enterprise emissions and improve environmental quality,the use of electricity data,which is easily accessible and has large amounts of data,can be utilized to study various algorithms to make the prediction of enterprise emissions more accurate and to monitor enterprise emissions.This article aims to use enterprise emissions and electricity consumption data,combined with the advantages of ARMA algorithm and SAX algorithm,to construct a SAX-ARMA hybrid model for monitoring and early warning of enterprise emissions.A case study was conducted using electricity consumption data from specific pollution enterprises in a certain region as the research object.The predicted results were compared with the actual data and the threshold set,demonstrating the effectiveness and feasibility of this method in practical applications,and achieving short-term prediction of emissions data for pollution enterprises,thereby strengthening the joint prevention and control of pollution for key enterprises.Through a comparative analysis of the prediction results of the ARMA model and the SAX-ARMA hybrid model,as well as the values of evaluation indicators of the models,it was found that the SAX-ARMA hybrid model had a higher degree of overlap between the predicted values and the actual values compared to the ARMA model.The RMSE and MAPE values of the SAX-ARMA hybrid model were lower than those of the ARMA model by 0.151 and 3.304,respectively.In conclusion,the SAX-ARMA hybrid model is more suitable for the research content of this article.
SAX-ARMAelectricity big dataenvironmental protectionpollution emission
NETL
NSTL
万方数据
