Abstract <p>Photovoltaic modules installed in dense urban environments often operate under high irradiance and limited convective cooling, leading to elevated cell temperatures and reduced power output, requiring research on thermal dissipation to keep the temperatures under near-optimal conditions. Most data acquisition on photovoltaic modules focus on electrical variables and global temperature measurements, limiting research on new cooling systems. This work presents an integrated software and thermal imaging framework to support the experimental evaluation of passive PV cooling systems in intelligent urban environments. The proposed solution combines an Internet of Things‑based data acquisition system combined with a thermal image processing pipeline. The data acquisition system supports dual dense temperature sensor grids on the back of cooled and reference solar modules, acquisition of electrical and environmental variables, configurable polling policies, and multiple data sinks including a time‑series database and cloud‑based visualization dashboards. The thermal imaging framework provides multi‑modal calibration between radiometric images and onsite temperature measurements, online generation of temperature maps, and computation of similarity metrics and spatial statistics that characterize thermal gradients, hot spots, and differences between cooled and noncooled modules. The resulting platform is reusable and extensible, enabling rapid integration of new sensors, cooling concepts and analysis routines, and lays the groundwork for creating rich multi‑source datasets to support future model‑based and data‑driven studies of photovoltaic thermal behavior in urban setting.</p>