Enterprise Pollution Emission Monitoring System Based on Deep Learning of Power Data

Abstract

The current situation of polluting enterprises is that they are numerous and widely distributed. The traditional supervision means only have regular inspection and public reporting, which is difficult to achieve effective monitoring. Based on the existing hardware of intelligent electricity meters, combined with Internet of things technology, Convolution Recurrent Neural Network, Long Short-Term Memory and other technologies, we build an automatic monitoring system for pollution enterprises based on power data. The system can automatically identify different types of equipment according to the load characteristics. By comparing and analyzing the operation of the enterprise’s pollutant production equipment and pollutant treatment equipment, it can detect the illegal sewage discharge behavior of enterprises in time. The experimental verification shows that the overall recognition mean square error of the model is only 0.5, and the accuracy of the model is higher than that of RNN model and LSTM model. The system can accurately and timely detect violations, filling the regulatory loopholes of the Environmental supervision department for enterprises.