Photovoltaic power forecasting using quantum machine learning

Predicting solar panel power output is crucial for advancing the transition to renewable energy but is complicated by the variable and non-linear nature of solar energy. This is influenced by numerous meteorological factors, geographical positioning, and photovoltaic cell properties, posing significant challenges to forecasting accuracy and grid stability. Our study introduces a suite of solutions centered around hybrid quantum neural networks designed to tackle these complexities. The first proposed model, the Hybrid Quantum Long Short-Term Memory, surpasses all tested models by achieving mean absolute errors and mean squared errors that are more than 40% lower. The second proposed model, the Hybrid Quantum Sequence-to-Sequence neural network, once trained, predicts photovoltaic power with 16% lower mean absolute error for arbitrary time intervals without the need for prior meteorological data, highlighting its versatility. Moreover, our hybrid models perform better even when trained on limited datasets, underlining their potential utility in data-scarce scenarios. These findings represent progress towards resolving time series prediction challenges in energy forecasting through hybrid quantum models, showcasing the transformative potential of quantum machine learning in catalyzing the renewable energy transition.

@article{sagingalieva2025_2312.16379,
  title={ Photovoltaic power forecasting using quantum machine learning },
  author={ Asel Sagingalieva and Stefan Komornyik and Ayush Joshi and Christopher Mansell and Karan Pinto and Markus Pflitsch and Alexey Melnikov },
  journal={arXiv preprint arXiv:2312.16379},
  year={ 2025 }
}