  Accurate solar power forecasting is pivotal for the global transition towards
sustainable energy systems. This study conducts a meticulous comparison between
Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory
(LSTM) models for solar power production forecasting. The primary objective is
to evaluate the potential advantages of QLSTMs, leveraging their exponential
representational capabilities, in capturing the intricate spatiotemporal
patterns inherent in renewable energy data. Through controlled experiments on
real-world photovoltaic datasets, our findings reveal promising improvements
offered by QLSTMs, including accelerated training convergence and substantially
reduced test loss within the initial epoch compared to classical LSTMs. These
empirical results demonstrate QLSTM's potential to swiftly assimilate complex
time series relationships, enabled by quantum phenomena like superposition.
However, realizing QLSTM's full capabilities necessitates further research into
model validation across diverse conditions, systematic hyperparameter
optimization, hardware noise resilience, and applications to correlated
renewable forecasting problems. With continued progress, quantum machine
learning can offer a paradigm shift in renewable energy time series prediction,
potentially ushering in an era of unprecedented accuracy and reliability in
solar power forecasting worldwide. This pioneering work provides initial
evidence substantiating quantum advantages over classical LSTM models while
acknowledging present limitations. Through rigorous benchmarking grounded in
real-world data, our study illustrates a promising trajectory for quantum
learning in renewable forecasting.
