Prediction of solar direct beam transmittance derived from global irradiation and sunshine duration using anfis

Abstract

This work describes the application of models to estimate the transmitted fraction of direct solar irradiation into normal incidence (Ktb) as a function of the atmospheric transmissivity (Kt) and the insolation ratio (n/N). In the first model, the values of Ktb and Kt in the hourly (h) and daily (d) partitions were correlated using polynomial regression. In the second model, Ktb and n/N in the daily partition were correlated through linear regression. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to estimate the obtained values Ktb. The performance of ANFIS1 (as a function of Kt hourly), ANFIS2 (as a function of daily Kt), ANFIS3 (as a function of daily n/N), and ANFIS4 (as a function of the ratio of Kt and daily n/N) are compared to statistical models. Two databases, defined as a typical year and an atypical year, are adopted to validate the models and ANFIS. The models were validated comparing the estimate and measurements using the statistical indicators rMBE and rRMSE. The statistical models as a function of Kt resulted in hourly average (Ktbh): 〈rMBE〉=−4.08±2.18% and 〈rRMSE〉=25.97±0.90%; daily (Ktbd): 〈rMBE〉=−4.93±4.89% and 〈rRMSE〉=22.06±2.06%. The model as a function of the insolation ratio resulted in a daily average (Ktbd): 〈rMBE〉=−3.01±2.62% and 〈rRMSE〉=24.15±0.30%. The rMBE of models using ANFIS are lower than those of statistical models. The rRMSE value using ANFIS1 model was 〈24.45%〉, while using ANFIS2, ANFIS3, and ANFIS4 models, they resulted on 〈16.47%〉, 〈19.60%〉, and 〈14.60%〉 in average, respectively, lower than the statistical models. ANFIS's performance is superior to statistical models.