Short-term data center load forecasting method based on data-physics hybrid driven framework

With the rapid growth of global digitalization, accurate short-term load forecasting for data centers (DCs) is crucial for efficient energy management and carbon reduction. However, existing methods usually treat DC loads as homogeneous, ignoring the distinct dynamics between non-cooling and cooling systems. This study proposes a data–physics hybrid forecasting framework for decoupled DC load prediction. The total load is first decomposed into non-cooling and cooling components according to their operational characteristics. For the non-cooling load forecasting, a DLinear–GRU fusion model is developed to jointly capture the linear trends and nonlinear temporal dependencies. For the cooling load forecasting, a physics-based Equivalent Thermal Parameter (ETP) model is derived, explicitly coupling the non-cooling forecast and ambient temperature variations. The integrated hybrid framework effectively combines data-driven and physics-based modeling. Case studies on measured DC data from North-west China show that the proposed framework achieves higher forecasting accuracy than existing models and exhibits a strong robustness under limited data, maintaining stable performance even with scarce training samples.