Quantum and classical hybrid models for wind turbine energy forecasting in an HPC–Edge architecture
HPC-Edgeアーキテクチャにおける風力タービンエネルギー予測のための量子・古典ハイブリッドモデル (AI 翻訳)
Hosseini MA, Rivas G, López-Blanco R, Prieto J
🤖 gxceed AI 要約
日本語
本研究は、量子機械学習を用いたハイブリッドHPC-Edgeアーキテクチャによる風力発電予測を提案。実データでの評価により、エッジLRモデルの残差を量子SVRが補正する方式が精度向上に有効であることを示した。
English
This study proposes a hybrid HPC-Edge framework integrating quantum-enhanced support vector regression for wind power forecasting. Using a real-world SCADA dataset, it shows that quantum residual correction significantly improves prediction accuracy over standalone edge models.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再生可能エネルギー導入拡大において、風力発電の予測精度向上は系統安定化と運用効率化に貢献する。本手法はエッジコンピューティングと量子技術の融合という点で、日本のスマートグリッド技術にも応用可能性がある。
In the global GX context
Accurate wind power forecasting is critical for integrating renewables into the grid. This hybrid quantum-classical approach demonstrates a practical pathway to enhance prediction accuracy, which is globally relevant for renewable energy management and grid stability.
👥 読者別の含意
🔬研究者:This paper provides a novel hybrid quantum-classical framework for time-series forecasting, relevant for researchers in renewable energy and machine learning.
🏢実務担当者:Utility companies and wind farm operators can use the proposed edge-based approach for low-latency forecasting, with quantum correction for higher accuracy.
🏛政策担当者:Policymakers supporting renewable energy integration can note the potential of quantum-enhanced forecasting to improve grid reliability.
📄 Abstract（原文）
<h4>Background: </h4> 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.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.12688/openreseurope.23182.1first seen 2026-05-20 04:37:28 · last seen 2026-05-31 04:21:25
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