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Photovoltaic power forecasting using quantum machine learning
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DOI：10.1016/j.solener.2025.114016.png)
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Abstract
En 中文
• 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.
Keywords:
Quantum machine learning
Hybrid quantum neural network
Quantum depth-infused layer
Quantum LSTM
Solar energy
Photovoltaic power
Time series
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