MDF-iTransformer: Multi Data Fusion-Based iTransformer for Load Prediction of Zero-Carbon Emission Integrated Energy System in Urban Park

To predict the output power of integrated energy systems (IES) under zero-carbon conditions, this research presents a Multi Data Fusion-based iTransformer prediction network (MDF-iTransformer). The network uses Multivariate Singular Spectrum Analysis (MSSA) to identify nonlinear relationships among variables and extract dynamic features from multi-modal data. It integrates an embedding block and multivariate attention module into the iTransformer network to capture complex patterns and long-term temporal dependencies in multi-dimensional data, thereby extracting dynamic features across different time scales and spatial dimensions. Subsequently, to address the issue of imbalanced datasets, the improved K-means-SMOTE (KS) algorithm is adopted to augment the number of small-class samples, effectively reducing model bias. Experimental results indicate that the proposed MDF-iTransformer achieves a root-mean-square error (RMSE) of 7.2 kW, mean absolute error (MAE) of 5.6 kW, mean absolute percentage error (MAPE) of 2.7%, and an R-squared value (R2) of 0.92 for a 1 h prediction horizon. It still maintains an RMSE of 14.4 kW, MAE of 11.9 kW, MAPE of 3.68%, and R2 of 0.74 at the 10 h horizon, with cross-season load forecasting errors consistently below 4%. Compared with other algorithms, MDF-iTransformer demonstrates higher accuracy and stronger robustness, playing a crucial role in the optimal operation of integrated energy systems.