Estimates of monthly global solar irradiation using empirical models and artificial intelligence techniques based on air temperature in Southeastern Brazil.

This study aimed to assess monthly average daily global solar irradiation ( H g m) estimates in Southeastern Brazil from empirical models and artificial intelligence (AI) techniques using extreme air temperatures (maximum—T_x and minimum—T_n) and extraterrestrial solar irradiation ( H 0 m ) and evaluate the geographic and climate factors in the performance of the models or the AI techniques. The H g m , T_x and T_n data series from 95 automatic weather stations of the Brazilian National Institute of Meteorology (INMET) were used. The Hargreaves-Samani (HS) and Bristow-Campbell (BC) models were fitted, and AI techniques (artificial neural network—ANN and support vector machines—SVM) were trained, and assessed according to local climate conditions. Three input schemes in the AI techniques were assessed: (i) H 0 m , T_x, and T_n; (ii) H 0 m and air temperature amplitude (ΔT); and (iii) H 0 m , T_x, T_n, and ΔT. The coefficients of the HS (0.134 ≤ k_r ≤ 0.262) and BC (0.539 ≤ β₀ ≤ 0.796; 0.004 ≤ β₁ ≤ 0.843; 0.35 ≤ β₂ ≤ 2.60) models and their performance were influenced by climate, continentality/maritime effects, altitude and weather systems. The performance of the HS model was inferior to that of BC model and the SVM and ANN techniques. Overall, the SVN and ANN techniques performed better than the BC method. However, BC had similar or superior precision and accuracy to SVM and ANN for some climatic conditions and combinations of input variables. The quality of the H g m series, the combination of input variables, and climate influenced the estimates of the SVM and ANN techniques. [ABSTRACT FROM AUTHOR]