Short-term electric load forecasting is essential to the secure and stable operation and economic dispatch of power systems, and its accuracy directly affects grid dispatch decisions and operational efficiency. To address the inadequate modeling of heterogeneity among historical exogenous variables, the underutilization of known future calendar information, and the difficulty of correcting local biases in multi-step forecasts, this paper proposes KCD-GEIRTimeXer, a short-term electric load forecasting method built upon TimeXer. Before exogenous variable embedding, a Grouped Exogenous Importance Recalibration (GEIR) module is introduced. Historical exogenous variables are first grouped a priori according to their sources and physical meanings. Importance scores are then computed at the feature, variable-group, and time-step levels, and the corresponding exogenous variable weights are obtained through a bounded residual gating mechanism. At the prediction stage, a Known Calendar–Causal Dual Correction (KCD) module is further incorporated. The module refines the initial forecasts using known future calendar variables and causal statistical features derived exclusively from historical load observations, thereby mitigating local biases in multi-step forecasting. Experiments on the publicly available Panama electricity load dataset demonstrate that the proposed model outperforms all baseline models. Averaged over three random seeds, KCD-GEIRTimeXer reduces MSE, RMSE, MAE, and MAPE by 26.25%, 14.12%, 13.10%, and 12.91%, respectively, compared with TimeXer.
With the widespread adoption of cloud computing technology, modern cloud platforms have become increasingly complex and dynamic, posing significant challenges for efficient resource management. Accurate forecasting of cloud resource loads has therefore become essential for improving service quality, optimizing resource utilization, and reducing operational costs. To address the intrinsic characteristics of cloud load time series, including nonlinear fluctuations, multi-scale temporal dependencies, and redundant high-dimensional features, this paper proposes the MST-iTransformer model, which integrates multi-scale temporal encoding, sparse attention, and adaptive feature selection mechanisms. Specifically, a multi-scale temporal encoding module is developed to capture and fuse temporal dependencies across multiple periodic scales. Furthermore, an adaptive feature selection module is introduced to dynamically assign importance weights to resource features, enhancing informative variables while suppressing redundant ones. Meanwhile, a sparse attention mechanism is incorporated to reduce computational overhead while maintaining forecasting accuracy. The proposed model is evaluated on the Alibaba Cluster Trace dataset. Experimental results demonstrate that MST-iTransformer achieves MSE, RMSE, and MAE values of 0.5559, 0.7456, and 0.4968, respectively. Compared with the original iTransformer, the proposed model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency. These improvements provide reliable prediction support for resource scheduling and elastic scaling in cloud data centers.
