Regional Spatiotemporal Joint Rolling Load Forecasting Based on Stacking Ensemble Learning
Short-term load forecasting faces significant challenges due to the spatiotemporal heterogeneity of modern power systems with high renewable penetration. This study proposes a Stacking ensemble learning model that integrates spatiotemporal joint rolling sampling to enhance forecasting accuracy. The sampling scheme utilizes recent load data from other load zones to predict the target zone, maximizing the use of time-sensitive information. Heterogeneous base learners include eXtreme Gradient Boosting (XGBoost), Huber regression, Elastic Net (EN), Back Propagation Neural Network (BPNN), and Elman neural network. Hyperparameters are optimized via Bayesian optimization with Hyperband (BOHB) and cross-validation. A meta-learner based on a convolutional neural network-bidirectional long short-term memory-multi-head attention (CNN-BiLSTM-MultiHeadAttention) architecture performs deep feature fusion. Validation on a real-world load dataset from southern China demonstrates that the proposed model outperforms conventional sampling methods and common models, particularly in handling step loads and non-stationary fluctuations. The results confirm the feasibility and superiority of the integrated approach, achieving significant improvements in prediction accuracy and robustness.