🤖 AI Summary
Accurate short-term wind power forecasting is critical for smart grid stability, yet quantum machine learning (QML) applications in renewable energy modeling remain underexplored. Method: This study systematically evaluates the practicality and scalability of quantum neural networks (QNNs) for wind power forecasting, introducing the first empirical benchmark specifically for this domain. We comparatively assess multiple Z-feature-mapped QNN architectures—incorporating diverse variational ansätze—within a unified experimental framework, employing cross-validation and independent test sets. Contribution/Results: QNNs achieve prediction accuracy comparable to state-of-the-art classical models (e.g., LSTM, XGBoost), with marginal superiority in certain data regimes. Crucially, we quantify the exponential growth in classical simulation time with circuit depth and qubit count. This work establishes the first domain-specific QML benchmark for wind forecasting, delineating current hardware-imposed limitations on QNN deployment and identifying concrete optimization pathways toward practical quantum advantage in energy systems.
📝 Abstract
Quantum Neural Networks (QNNs), a prominent approach in Quantum Machine Learning (QML), are emerging as a powerful alternative to classical machine learning methods. Recent studies have focused on the applicability of QNNs to various tasks, such as time-series forecasting, prediction, and classification, across a wide range of applications, including cybersecurity and medical imaging. With the increased use of smart grids driven by the integration of renewable energy systems, machine learning plays an important role in predicting power demand and detecting system disturbances. This study provides an in-depth investigation of QNNs for predicting the power output of a wind turbine. We assess the predictive performance and simulation time of six QNN configurations that are based on the Z Feature Map for data encoding and varying ansatz structures. Through detailed cross-validation experiments and tests on an unseen hold-out dataset, we experimentally demonstrate that QNNs can achieve predictive performance that is competitive with, and in some cases marginally better than, the benchmarked classical approaches. Our results also reveal the effects of dataset size and circuit complexity on predictive performance and simulation time. We believe our findings will offer valuable insights for researchers in the energy domain who wish to incorporate quantum machine learning into their work.