Quantifying the Economic Loss and Operational Implications of Air Pollution on Grid-Connected PV Systems in the Arabian Peninsula: A Machine Learning-Based Analysis

The Arabian Peninsula is one of the regions in the world with the highest potential for solar energy development in the sense that the levels of solar irradiance are very high, making it an ideal site to install photovoltaic (PV) systems. However, this huge potential is seriously undermined by enduring environmental problems, especially the high levels of airborne particulate matter and anthropogenic pollutants that together reduce the intensity of the sun and contribute to the rapid soiling of PV modules. Though the current literature has largely concentrated on the quantification of the technical performance instability of PV systems caused by soiling phenomena, the associated extensive analysis in terms of quantifying the technical losses into quantifiable economic and operational effects on a regional level is strikingly lacking in the literature. This study builds a combined machine learning system in order to quantitatively measure the economic losses attributable to air pollution for utility-scale, grid-connected PV systems across the Gulf Cooperation Council (GCC) member states. We developed and strictly tested a Random Forest regression model with a remarkable coefficient of determination (R 2 ) of 98.24 that was trained on a large dataset of meteorological parameters, real-time air pollution data (PM2.52.5, PM1010, SO22, NO22, O33, CO) and the most important operational features of solar panels that occurred between 2018 and 2020. With the help of this validated predictive model, we conducted an advanced counterfactual analysis by modeling the potential power production under hypothetical conditions of clean atmosphere benchmarks. We show that air pollution is one of the contributors to the loss of 8.5% to 12.3% of annual energy production in the region; or more simply, it is a significant loss that can translate to huge financial fines, which can significantly affect the economics of projects. In addition, we propose a new data-driven predictive cleaning scheduling algorithm that proves capable of cutting operational expenditures (OPEX) by up to 25 percent relative to traditional calendar-driven cleaning schedules. The findings are empirically based, critical to renewable energy investors, utility grid operators, and policymakers, and they categorically highlight the significant economic necessity to set up wide-ranging air pollution reduction measures, even as operation and maintenance (O&M) protocols in arid, dust-prone geographical settings run to their optimum.