Zero-Carbon Parks’ Electric Load Forecasting Considering Feature Extraction of Multi-Type Electric Load and Dual-Layer Optimization Modal Decomposition

The construction of zero-carbon parks has become an urgent priority. Electric load forecasting plays a decisive role in enabling the efficient operation of industrial parks; however, the complexity of electric load features within the parks has limited the accuracy of electric load forecasting. A novel electric load forecasting framework with feature extraction (TPE-AVMD-BiLSTM with feature extraction) is proposed to improve the forecasting accuracy. This framework combines feature extraction, decomposition with TPE optimization, and BiLSTM prediction. Together, these components work to remove the influence of irrelevant or redundant features. To verify the superiority of the proposed model, ablation experiments were carried out. The annual hourly electric load (8760 h) of typical industries was predicted within the park, including a data center, chemical manufacturing company, residence, shopping mall, cement manufacturing plant, and hospital. The results showed that the proposed model achieved high accuracy for all typical industries (R² > 0.9891, E_MAE < 0.3714, E_RMSE < 0.4694), indicating that the forecasting has excellent coverage performance. The performance of the proposed model over the feature-free baseline confirms that incorporating more correlated features enhances prediction stability. The framework presents a viable solution for achieving accurate electric load forecasting within zero-carbon parks.