Determinants of diffuse solar radiation in urban and peatland areas based on weather and air pollutants

Understanding solar radiation variability is essential for efficiently planning and managing solar energy systems. The transmission of solar radiation to the ground is generally affected by a variety of factors. This research deals with the impacts of weather and air pollution on the amount of diffuse solar radiation across two distinct environments, i.e., urban and peatland areas. Two stations in Jakarta, Kemayoran and Jagakarsa represent the urban area, while two stations in South Kalimantan, Pinang Habang and Jambu, represent the peatland area. Three machine learning-based models were used to estimate the amount of diffuse solar radiation related to weather and air pollution, namely Random Forest (RF), K-nearest neighbor (KNN), and Light Gradient Boosting Machine (LGBM). Five experiments were carried out using various combinations of predictor variables, including temperature, air pollutants, and cloud cover. The results of the experiments highlighted the significance of pollutants as predictive factors. All models demonstrated reliable results in capturing the variability of diffuse solar radiation in four stations, revealing that urban areas receive approximately half the amount of diffuse solar radiation compared to peatland areas, despite sharing a similar annual pattern. Among the models, RF model achieved the highest correlation coefficient with actual values, yielding the least error. Among the sites studied, the predictions from peatland areas closely aligned with the reference pattern. Furthermore, this study found that CO is the primary factor in predicting diffuse solar radiation in urban areas. Differently, PM_2.5 mostly impact the diffuse solar radiation in rural areas. This research underscores the critical role of air pollutants, particularly CO and PM_2.5, in determining solar radiation levels, which in turn affects the efficiency of solar energy systems.