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Large-scale solar promises a low-carbon energy alternative. However, solar production in North America given anticipated climate change has been studied only seasonally in terms of solar irradiance. This work integrates more of the predictive potential of climate-change models by exploring other environmental variables, such as humidity and temperature. Here, a Continental US (CONUS) model is produced by deep learning using 2593 NREL simulated solar power stations. Daily forecasts using 17 Global Climate Models (GCM’s) through 2099 are summarized monthly. Results suggest power production factors change between +4 % and 􀀀 19 % over 93 years. These results suggest more, but still modest, potential declines than previous solar irradiance-based studies. The modest impact is encouraging. For some areas, climate model variability unfortunately yielded statistically insignificant trends and practical application is less clear. For future evaluations, this work suggests the potential importance of additional variables, monthly interval summary, and accounting for model variability. 

In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing cleanhybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation.