A new arXiv preprint introduces Solar Intelligence, a language model built specifically for solar energy decision support. The authors argue that existing solar dashboards present data without explanation and that general-purpose models answer solar questions without citing evidence. Their system is positioned as a domain-focused alternative aimed at producing better-grounded responses for energy planning.
A new arXiv paper analyzes the trade-offs of using ensembles for solar power forecasting, noting that added components can raise computation without improving accuracy. The authors ran matched comparisons and ablation tests on a fixed set of diverse predictors, using hourly data to isolate each component's contribution. The work aims to clarify when ensemble complexity is justified for photovoltaic prediction.
A revised arXiv preprint describes a method that combines deep feature extraction techniques to identify surface defects on solar panels. The authors motivate the work by noting that manual inspection of large-scale solar plants is labor-intensive, slow, and expensive. The approach aims to support automated monitoring for plant efficiency and reliability.