An Ensemble Deep Learning for Carbon Emission Prediction in Urban Industrial Parks Based on NSAGN and EA-BiLSTM

To address the challenges of carbon emission prediction in complex energy systems within industrial parks, this paper proposes a Categorical Boosting (CatBoost) ensemble learning model that integrates a node-semantic attention graph network (NSAGN) and an external attention-based bidirectional long short-term memory (EA-BiLSTM). First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique is employed to preprocess the data, effectively eliminating anomalies and noise in nonstationary multi-energy load and carbon emission data, thereby providing high-quality input data for subsequent prediction models. Second, the NSAGN is constructed to enhance the ability to capture associations in heterogeneous data, enabling accurate extraction of hierarchical features related to energy consumption and carbon emissions. Subsequently, an external attention (EA) mechanism is introduced into the BiLSTM to achieve adaptive weight allocation, further improving the model’s ability to capture local and global feature variations in carbon emission data and enhancing its effectiveness in mining time-series features. Finally, CatBoost is utilized to perform weighted integration of the predictions from the two branches, fully leveraging the advantages of time-series analysis and nonlinear learning of heterogeneous data, thereby improving the accuracy of carbon emission prediction. Comparative experimental analysis demonstrates that the proposed model outperforms comparative models in terms of evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ([Formula: see text]) across different prediction horizons, enhancing the accuracy and robustness of carbon emission prediction. This provides strong support for carbon emission assessment in industrial parks.