Precision Comparison of Intelligent Measurement Methods for Enterprise Pollutant Emissions Based on Power Big Data
The amount of pollution discharge data from power enterprises is relatively large and comes from a wide range of sources.If valuable information cannot be extracted from these data,it will lead to significant errors in the calculation results of pollutant discharge from page cut-ting.How to dynamically calculate the pollution discharge of electric furnace enterprises using the most suitable method through power big data is a difficult problem.Therefore,a comparative study on the accuracy of intelligent calculation methods for enterprise pollutant emissions based on power big data will be conducted.Taking the calculation of pollutant emissions from a foundry in Chongqing as an example,Al intelligent recognition technology was used,combined with traditional RBF neural network models,BP neural network models,and multiple stepwise re-gression models,to train and calculate the pollutant emissions for each period from December 2021 to June 2022.Based on the comparative a-nalysis of MAE,MAPE,and RMSE,the calculation accuracy results show that RBF neural network model＞BP neural network model＞multi-ple stepwise regression model.The research results indicate that using electricity big data combined with appropriate algorithms can calculate the pollutant emissions of electric furnace enterprises in real-time,providing technological support for precise and scientific pollution control.
power big datamodel calculationRBF neural networkBP neural networkdynamic blowdown of electric furnace enterprises
万方数据
维普
