A decision-support system for solar energy resource planning in arid desert regions

This study presents a quantitative decision-support system for solar resource assessment and forecasting in desert environments, developed using minute-resolution data from 13 monitoring sites in a flat, aerosol-rich, low-cloudiness region. By evaluating the spatial variability and distance-dependent errors of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), the research identifies high spatial uniformity for GHI and significant local variability for DNI. Statistical analyses — including Pearson correlation and variability metrics — demonstrate that extrapolation errors for GHI are modest, while DNI errors increase sharply with distance, highlighting the importance of strategic site selection and uncertainty quantification in energy yield assessments. In addition to reanalysis data from CAMS, the study assesses three interpolation methods (IDW, gravity-based IEA, and CSV), finding that the Cumulative Semivariogram (CSV) method delivers superior accuracy, particularly for DNI, and is well-suited for sparse monitoring networks. The presented results provide actionable metrics for optimizing monitoring station spacing and inform cost-effective investment decisions, supporting scalable and resilient solar infrastructure planning in arid and semi-arid regions globally. Additionally, The approaches and findings offer practical guidance for energy planners and investors to reduce financial risk and enhance the reliability of solar project development beyond the immediate study area.