The power and utility sector exerts a formidable influence on the global economy engendering profound contributions to overarching expansion. Encompassing the entirety of energy origination, dissemination, and conveyance this sector embraces both orthodox and sustainable sources incorporating solar, wind, biomass, and geothermal energies. Ecological apprehensions have galvanized governments and corporate entities on a global scale to pivot their emphasis towards ecologically viable energy methodologies wherein solar energy has emerged as a preeminent selection. Though applications of solar energy display variegation its transformation into electrical potentiality via solar panels predominates. The progression of time begets the dissipation of panel efficiency predominantly ascribed to inherent anomalies. Conventional manual scrutations levy considerable functional and capital outlays thereby imposing tension upon profitability and steadfastness. To transcend these quandaries an envisage of automated drone-centric scrutations leveraging artificial intelligence notably within convolutional neural networks is posited. This paradigm evinces an 85% precision rate and an 83.33% exactitude in pinpointing defective solar panels thereby exuding an illustrious semblance of model stability. Aspirations for the future encompass model honing through hyper-parameter finessing and assimilation of more expansive data sets thereby ameliorating precision to loftier echelons.
