Intelligent Prediction of Pollution Emissions Based on Machine Learning and Power Data

Traditional pollution emission prediction methods are difficult to effectively handle complex power data and have insufficient prediction accuracy. To solve the above problems, this study adopts a machine learning-based method and introduces a prediction model that integrates convolutional neural network (CNN) and support vector regression (SVR), using the advantages of both to improve the prediction ability of pollution emissions. First, massive power data and corresponding pollution emission data are collected from multiple sources such as power system databases and environmental monitoring stations. Then, the collected data are cleaned to address problems such as noise, missing values, and outliers. After that, the data features are mapped to a unified interval, and the convolutional layer and pooling layer of CNN are used to automatically extract the deep features in the power data. Then, the Relief algorithm is used to screen out the features that have a significant impact on pollution emissions, and the feature vector of the input model is further optimized. Next, a CNN-SVR hybrid model is built, and the filtered feature vector is input into the CNN part for deep feature mining and abstract representation. The output features are then passed to the SVR part for regression prediction. The absolute error of the CNN-SVR model has been reduced, the average prediction delay has been reduced from 750.5 milliseconds to 317.3 milliseconds, and the standard deviation of the predicted value has also been reduced from 0.602 tons to 0.93 tons to 0.245 tons to 0.522 tons. These results demonstrate the effectiveness and superiority of the CNN-SVR model in the field of power data and pollution emissions prediction.