This study presents a comprehensive framework for short-term solar irradiance forecasting in the Adana Organized Industrial Zone, one of Türkiye’s most solarrich regions. High-resolution irradiance data collected throughout 2024 using ISO 9060–compliant pyranometers were analyzed to capture seasonal and daily variations, with annual cumulative global horizontal irradiance measured at ~1346 kWh/m2. Two machine learning approaches were applied to predict hourly irradiance: Random Forest (RF) regression and multilayer perceptron (MLP) neural networks. Input features included persistence-based irradiance values, diurnal and seasonal indicators, and time-series variables. Model performance was evaluated using mean absolute error, root mean square error (RMSE), and R2 metrics. Results indicated that RF achieved superior accuracy (R2 = 0.891, RMSE = 0.472 kWh/m2) compared to MLP (R2 = 0.874, RMSE = 0.514 kWh/m2), highlighting the robustness of ensemble methods for short-term forecasting. Uncertainty analysis confirmed that measurement errors and cloudy-day conditions remain key challenges. Unlike previous studies, this work provides one of the first high-resolution, region-specific datasets for industrial-scale solar forecasting in Türkiye. Overall, the findings demonstrate that data-driven forecasting can effectively support photovoltaic (PV) system operation, smart grid integration, and regional energy planning, providing valuable insights for the sustainable energy transition in Türkiye.
Rabia Sultan Yıldırım Varlı (Fri,) studied this question.