Performance Prediction Based on Solar Photovoltaic Siting Using Geographic Information Systems and Machine Learning

Expanding solar power plants requires strategic planning that accounts for future climatic, environmental, and topographic conditions. This study develops an integrated Geographic Information Systems (GIS), multi-criteria decision analysis (MCDA), and machine-learning framework to identify priority locations for solar PV development in Iraq under the ACCESS-CM2 SSP2-4.5 climate projection for 2050. Support vector machine (SVM), random forest (RF), and gradient-boosted trees (GBT) classifiers were evaluated against MCDA-derived reference classes using five-fold spatial block cross-validation as the primary validation procedure; the fixed stratified 70:30 split was retained only for comparison. Their spatial outputs were integrated through GIS-based intersection to identify consensus suitability zones. Under the adopted temperature- and dust-correction model and its reference parameter assumptions, the modeled mean PV-efficiency indicator was about 14.3%, and the modeled mean daily energy-yield indicator was about 872 Wh/m²/day at a mean projected air temperature of 33.3 °C and a mean DUEXTTAU index of approximately 0.20. These quantities are scenario-dependent screening-model outputs, not observed or field-calibrated measurements. The mean spatial-validation accuracies were 91.46 ± 2.90% for SVM, 84.03 ± 2.14% for RF, and 82.22 ± 4.47% for GBT; these values quantify spatially separated agreement with the MCDA-derived reference classes and do not constitute independent validation of physical site suitability. Accordingly, the resulting maps should be interpreted as a spatial screening and prioritization framework whose quantitative outputs remain conditional on the adopted climate projection, MCDA weighting scheme, PV-performance assumptions, and the absence of independent field-based ground truth.