Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Buildings in hot climate areas are responsible for high energy consumption due to cooling load requirements which lead greenhouse gas emissions. In order curtail the stress on national grid and reduce atmospheric emissions, it is of prime importance that buildings produce their own onsite electrical using renewable resources. Photovoltaic (PV) technology most favorable option electricity buildings. Installation PV modules roof has a twofold advantage acting as shading device requirement building while producing electricity. A ground coverage ratio provides more shading, but decreases efficiency system because self-shading modules. The aim this paper was determine optimal value gives maximum overall performance roof-mounted by considering surface parallel An unsupervised artificial neural network approach implemented Net levelized cost (Net-LCOE) optimization. gradient decent learning rule used optimize connection weights obtained. proposed optimized shown have many distinct advantages over typical ground-mounted configuration such 2.9% better capacity factor, 15.9% yield, 40% ratio, 14.4% less LCOE, 18.6% shorter payback period. research work validates area very useful meet demand