Predicting solar panel power output is crucial for advancing the energy
transition 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 over 40% lower mean absolute
and mean squared errors. The second proposed model, 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 a
stride towards resolving time series prediction challenges in energy power
forecasting through hybrid quantum models, showcasing the transformative
potential of quantum machine learning in catalyzing the renewable energy
transition.

通过混合量子神经网络，该研究提出了两种模型来预测太阳能电池板的功率输出，其中第一种模型表现最好，降低了超过 40% 的平均绝对误差和平均平方误差；第二种模型则在没有气象数据的情况下，以比传统模型低出 16% 的平均绝对误差预测光伏功率，展现出量子机器学习在可再生能源领域中的潜在用途。