| Summary: | This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of <i>Miscanthus x giganteus</i> (<i>MxG</i>), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of <i>MxG</i>. The models showed high predictive accuracy, achieving R<sup>2</sup> values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of <i>MxG</i> as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.
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