In the ever-changing energy landscape, renewable energy sources are starting to play an increasingly important role due to the depletion of fossil fuels and the ongoing climate crisis. The integration of large language models (LLMs) into the field of renewable energy can act as a catalyst for its worldwide adoption. This work examines the possibility of using LLMs in forecasting and predicting renewable energy systems (RES), addressing the significant variability in power generation output due to environmental factors and weather conditions. Accurate forecasting is essential for the efficient integration of renewable sources into the power grid. The study analyzes existing research on machine learning (ML), deep learning, and LLM algorithms applicable to renewable energy. It evaluates the predictive accuracy of various AI models, including the “Temporal Fusion Transformer” (TFT), which is a transformer-based time-series forecasting model, and traditional ML and statistical models. Additionally, the research outlines challenges hindering the effective use of LLMs in renewable energy forecasting. The outcome of this chapter is to highlight the potential that LLMs hold in accelerating renewable energy integration into the system through providing accurate forecasts for effective decision-making.