Road Thermal Collectors (RTCs) represent a promising multifunctional technology for urban energy systems, simultaneously addressing solar energy harvesting, urban heat island mitigation, and pavement durability enhancement. This study introduces a novel, computationally efficient mathematical model specifically developed for multi-layer RTC systems operating under real, highly variable environmental conditions, validated through a 72-hour continuous experimental campaign on an RTC prototype under real-world, fluctuating meteorological conditions. Implemented using an explicit finite volume method in Python, the model uniquely combines analytical transparency with computational efficiency, enabling extended transient simulations (up to seasonal or annual) and rapid parametric optimization studies that are prohibitively expensive with conventional three-dimensional computational fluid dynamics approaches. The model incorporates dynamic wind-dependent convection correlations, real-time sky temperature calculations, and complete thermal characterization of each structural layer, successfully predicting complex phenomena such as heat flow inversions during windy nighttime conditions. Extensive validation against experimental data from a multi-layer RTC prototype, conducted over multiple seasonal periods with real fluctuating meteorological conditions, demonstrates robust predictive accuracy (mean absolute error max value ∼2.2°C, R 2 min value ∼0.84). This research provides three primary contributions: a validated open-source framework enabling site-specific performance prediction across diverse geographical contexts; a transparent analytical formulation facilitating systematic parametric analyses essential for design optimization; and broad experimental datasets revealing non-linear meteorological-thermal interactions. The model computational efficiency transforms long-term performance prediction from expensive research tasks into practical engineering activities, establishing a foundation for advancing RTC technology from laboratory prototypes toward commercial deployment. Future research will leverage this framework for systematic sensitivity analyses and the integration of PCMs and hybrid PV-Thermal technologies. By incorporating multi-objective optimization via ANN and Genetic Algorithms, the model paves the way for Digital Twin applications and predictive maintenance. Ultimately, these advancements support the transition to commercial deployment, providing a robust foundation for sustainable energy planning in next-generation smart cities.
Biondi et al. (Sun,) studied this question.