Quantum and classical hybrid models for wind turbine energy forecasting in an HPC–Edge architecture

Background Accurate wind power forecasting is essential for ensuring the stability and efficiency of modern energy infrastructures. However, the nonlinear behavior and complex correlations present in wind turbine data make prediction increasingly challenging. Recent advances in Quantum Machine Learning (QML) suggest that quantum kernels can capture higher-order feature interactions, potentially improving forecasting performance over classical models. Methods We propose a hybrid High-Performance Computing–Edge (HPC–Edge) forecasting framework that integrates quantum-enhanced Support Vector Regression (QSVR) executed on HPC resources with lightweight Linear Regression (LR) deployed at the edge, close to the data source. The system follows a residual-learning strategy in which the quantum model predicts and corrects the residual errors of the edge-based LR model. The approach is evaluated using a real-world wind turbine SCADA dataset containing more than 50,000 ten-minute interval measurements collected in Türkiye. Results The results show that while edge-based LR models remain competitive for local, low-latency forecasting, the hybrid HPC–Edge architecture substantially improves predictive performance. Across multiple sampling configurations, the hybrid model achieves an RMSE of 117.95, an R 2 of 0.9638, and a SMAPE of 27.00%, outperforming both standalone edge models and standalone quantum models. Conclusions These findings demonstrate that combining edge-level efficiency with quantum-enabled residual correction provides a practical and effective pathway for integrating quantum regression techniques into operational wind power forecasting systems. The hybrid HPC–Edge approach improves accuracy without compromising deployability, highlighting its potential for real-world renewable energy applications.