Photovoltaic power forecasting using quantum machine learning

Accurate forecasting of photovoltaic power is essential for reliable grid integration, yet remains difficult due to highly variable irradiance, complex meteorological drivers, site geography, and device-specific behavior. Although contemporary machine learning has achieved successes, it is not clear that these approaches are optimal: new model classes may further enhance performance and data efficiency. We investigate hybrid quantum neural networks for time-series forecasting of photovoltaic power and introduce two architectures. The first, a Hybrid Quantum Long Short-Term Memory model, reduces mean absolute error and mean squared error by more than 40% relative to the strongest baselines evaluated. The second, a Hybrid Quantum Sequence-to-Sequence model, once trained, it predicts power for arbitrary forecast horizons without requiring prior meteorological inputs and achieves a 16% lower mean absolute error than the best baseline on this task. Both hybrid models maintain superior accuracy when training data are limited, indicating improved data efficiency. These results show that hybrid quantum models address key challenges in photovoltaic power forecasting and offer a practical route to more reliable, data-efficient energy predictions. • Quantum models cut PV power forecasting errors by over 40% vs. classical models. • Hybrid Quantum LSTM improves accuracy even with limited training data. • Sequence-to-Sequence quantum model predicts power without weather input. • Quantum Depth-Infused layers boost learning efficiency and model performance. • Hybrid quantum models offer a scalable, energy-efficient forecasting solution.