  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.
