Rooftop Photovoltaic (RTPV) systems have gained more interest due to modularity and environmental friendliness. This article proposes an RTPV system for fulfilling the load demand of the main campus of Assuit University. The proposed system's economic and technical feasibility is comprehensively explored, including the expense and reliability. The system's sizing is implemented as a constrained optimization challenge. Particle Swarm Optimization (PSO) is employed to identify the proposed RTPV modules' optimal quantity, considering expanding Assuit University until 2025. Robust and reasonably accurate load forecasting models are developed and examined, including medium and long terms, to identify the monthly/annual load peaks from 2019 to 2025. The sizing procedure's PSO outcomes are validated via their comparison with Software such as PVsyst and PVGIS. The results indicated the economic efficacy of the proposed RTPV system and the ability of PSO to yield improved sizing than the other Software because of the well-formed objective function.