Using artificial intelligence for enhancement of solar cell efficiency in the of Iraq

The southern region of Iraq contains substantial solar energy development opportunities because it receives high solar irradiance levels in extensive desert territories. The specific location factors affecting solar energy cell efficiency consist of solar irradiance levels together with temperature conditions dust concentrations and site shading conditions. Traditional site selection approaches neglect how these multiple factors interact with each other so they result in less than ideal solutions. The research seeks to boost solar energy cell performance by establishing an AI-based site selection strategy for south Iraq locations. A Random Forest machine learning method conducts an analysis of environmental elements geographical characteristics and infrastructure factors to calculate site suitability potential. The model proposal incorporates solar irradiance climatic condition data topographic features and land usage along with infrastructure proximity to build composite suitability measures for every potential location. The research identifies locations best-suited for solar energy projects to boost operational effectiveness and decrease both costs and energy generation costs. Through this research, a framework emerges for solar energy project deployment strategy which contributes to sustainable development and helps Iraq advance its renewable energy framework. Research findings confirm that AI-based site selection procedures can boost solar energy cell performance throughout south Iraq. The model uses Random Forest machine learning to integrate diverse data types which enables site suitability prediction together with optimized outcomes in energy production and economic affordability. Research confirms that the proposed method detects top solar installation sites simultaneously with its capability to boost Iraq's sustainable energy projects through maximizing performance with lower prices and reduced environmental impact.