Abstract
This research introduces an integrated, AI simulation-based solution and optimization framework for an off-grid solar system tailored for a residential unit in the mountainous region of Saint Catherine, Egypt. The novelty of this study lies in the integration of four simulation tools: Climate Consultant, Design Builder, PVSYST, and SketchUp within a single workflow, linking building energy performance, solar system design, and shading geometry analysis. A multi-objective optimization algorithm-Non-dominated Sorting Genetic Algorithm (NSGA-II)-was then utilized to co-optimize various architectural and system parameters, such as orientation, insulation, and glazing ratios, with the objectives of minimal annual energy consumption and enhanced comfort for the occupants. Contrary to the conventional approach that limits either building or system performance in isolation, the current research integrates passive design strategies coupled with Trombe wall, double low-E glazing, and optimized WWRs with smart operational controls comprising automated shading, hence creating an adaptive, energy-efficient model. Herein, the optimal scenario is associated with an annual energy demand of 5710 kWh and only 315 discomfort hours, while the corresponding solar microgrid, simulated in PVSYST, attains a performance ratio equal to 72.5% and provides a 77.4% cost saving over 20 years in comparison with grid electricity. Results have established a replicable computational workflow for energy-independent housing in remote areas with high irradiance and hence provide a novel contribution to sustainable design practices in Egypt. The study is limited to computational simulation and physical validation to confirm real world performance.