Accurately forecasting solar power generation is crucial in the global
progression towards sustainable energy systems. In this study, we conduct a
meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and
classical Long Short-Term Memory (LSTM) models for solar power production
forecasting. Our controlled experiments reveal promising advantages of QLSTMs,
including accelerated training convergence and substantially reduced test loss
within the initial epoch compared to classical LSTMs. These empirical findings
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. This pioneering work
provides initial evidence substantiating quantum advantages over classical
LSTM, while acknowledging present limitations. Through rigorous benchmarking
grounded in real-world data, our study elucidates a promising trajectory for
quantum learning in renewable forecasting. Additional research and development
can further actualize this potential to achieve unprecedented accuracy and
reliability in predicting solar power generation worldwide.Comment: 17 pages, 8 figure