Rooftop photovoltaic (PV) systems play a crucial role in improving renewable energy generation in urban settings, where land constraints and rising energy demands present significant challenges. As cities endeavor to meet sustainability goals and reduce carbon footprints, optimizing the efficiency of these systems becomes essential for maximizing their contribution to clean energy production. This study examines two primary strategies to enhance the energy conversion efficiency of rooftop PV systems: hardware innovations and software innovations. Within the realm of hardware solutions, this research explores photovoltaic integrated green roof systems (PVIGR) and panel-level optimizers (PLOs). The findings reveal that the optimal PVIGR configuration for Hong Kong’s climate—characterized...[ Read more ]

Rooftop photovoltaic (PV) systems play a crucial role in improving renewable energy generation in urban settings, where land constraints and rising energy demands present significant challenges. As cities endeavor to meet sustainability goals and reduce carbon footprints, optimizing the efficiency of these systems becomes essential for maximizing their contribution to clean energy production. This study examines two primary strategies to enhance the energy conversion efficiency of rooftop PV systems: hardware innovations and software innovations. Within the realm of hardware solutions, this research explores photovoltaic integrated green roof systems (PVIGR) and panel-level optimizers (PLOs). The findings reveal that the optimal PVIGR configuration for Hong Kong’s climate—characterized by Sedum as the ground cover, a tilt angle of 22 degrees, and a separation height of 0.9 meters—achieved an average efficiency of 19.90%, surpassing the 19.07% efficiency of traditional PV modules. The implementation of PLOs resulted in a 3.57% improvement in energy conservation efficiency. Notably, the SQT4 system, equipped with optimizers, exhibited a mean performance ratio of 0.76, significantly exceeding the SQT9 system, which operates without optimizers and attained a mean performance ratio of 0.61. On the software side, this research developed the PV Brick Schema Model to effectively organize metadata for 60 grid-connected rooftop PV stations at the Hong Kong University of Science and Technology (HKUST) campus. This model facilitates structured data representation and enhances interoperability through SPARQL queries. Additionally, an Automated Intelligent Fault Detection and Reporting System was implemented to streamline monitoring processes, generating detailed fault analysis reports and enabling timely interventions based on statistical algorithms. This research underscores the importance of both hardware advancements and intelligent software solutions in optimizing the performance of rooftop PV systems, ultimately contributing to more sustainable urban energy solutions.