A residual learning-based grey system model and its applications in Electricity Transformer's Seasonal oil temperature forecasting

Accurately predicting cross-regional electricity demand is crucial for efficient distribution management, but it remains challenging due to its complexity. Transformer oil temperature is a key indicator of operational status, and analyzing its seasonal variation is vital for addressing distribution issues. Grey models based on neural networks are effective for predicting nonlinear and small-scale datasets but are prone to overfitting. While residual networks help mitigate overfitting, their application to small-scale time series forecasting is still limited. To improve prediction accuracy for nonlinear and small-scale data, this study introduces residual learning into grey models, proposing a hybrid model. This model combines the feature-capturing ability of residual learning networks with the robustness of grey models, helping to reduce overfitting. The model is trained using the Adam algorithm, with parameters optimized by the Gridsearch algorithm. Performance is demonstrated using four seasonal datasets of transformer oil temperature. A comparison with 13 grey system models and 9 machine learning models shows that the proposed method outperforms the others. By calculating the percentage improvements of various metrics, the model demonstrates consistent performance gains. Sensitivity analysis reveals that the model's performance is sensitive to the number of neurons and network depth, with higher values significantly improving accuracy and robustness. The results confirm the model's effectiveness. This study fills the gap between neural grey models and residual networks, successfully applying the model to forecast the seasonal temperature trends of power transformers and providing a theoretical basis for addressing power distribution challenges.