Agent Systems and GIS Integration in Requirements Analysis and Selection of Optimal Locations for Energy Infrastructure Facilities

The dynamic development of agent systems and large language models opens up new possibilities for automating spatial and investment analyses. The study evaluated a reactive AI agent with an NLP interface, integrating Apache Spark for large-scale data processing with PostGIS as a reference point. The analyses were carried out for two areas: Nowy Sącz (36,000 plots, 7 layers) and Ostrołęka (220,000 plots). For medium-sized datasets, both technologies produced similar results, but with large datasets, PostGIS exceeded time limits and was prone to failures. Spark maintained stable performance, analyzing 220,000 plots in approximately 240 s, confirming its suitability for interactive applications. In addition, clustering and spatial search algorithms were compared. The basic DFS required 530 s, while the improved one reduced the time almost tenfold to 54–62 s. The improved K-Means improved the spatial compactness of clusters (0.61–0.76 vs. <0.50 in most base cases) with a time of 56–64 s. Agglomerative clustering, although accurate, was too slow (3000–6000 s). The results show that the combination of Spark, improved algorithms, and agent systems with NLP significantly speeds up the selection of plots for renewable energy sources, supporting sustainable investment decisions.