Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (H-D) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, H-D observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (H-D(h)) and global solar irradiation (H-G(h)) data collected between 2002 & horbar;2003 and 2007 & horbar;2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating K-D(h)(=H-D(h)/H-G(h)) (r(2) > 0.726, modified Willmott - d(m) > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating K-D(h) for hourly atmospheric transmittance (K-T(h)) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r(2) > 0.718, d(m) > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r(2) > 0.704, d(m) > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating K-D(h) superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for H-D estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.