Prediction of Carbon Emissions from Thermal Power Plants Based on PCA-BiLSTM

With the continuous development of artificial intelligence technology, the application of prediction technology in various fields is becoming increasingly widespread. Based on the methods of Principal Component Analysis (PCA) and Bidirectional Long Short Term Memory Neural Network (BiLSTM), principal component analysis is used to reduce the dimensionality of multiple features related to carbon emissions from thermal power plants, in order to reduce the correlation between data dimensions and features. Then, construct a BiLSTM model to learn complex patterns and dependencies in time series data, and predict the future carbon emissions of thermal power plants. The experimental results show that the prediction method based on PCA-BiLSTM performs well in predicting carbon emissions of thermal power plants, providing a feasible and effective solution for predicting carbon emissions of thermal power plants.