Abstract
The estimation of global solar radiation continues to play a fundamental role in solar engineering systems and applications. This paper compares various models for estimating the average monthly global solar radiation on horizontal surface for Akure, Nigeria, using solar radiation and sunshine duration data covering years 1981 to 1995. The analysis was performed using Angstrom models, two dimensional principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The performance of the models were tested using statistical indicators such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and correlation coefficient (CC). The results indicated that ANFIS and linear regression analysis provide relatively higher degree of prediction, with the performance of ANFIS slightly better.
Highlights
Global solar radiation (GSR) measurements play a fundamental role in the design of solar engineering projects and in evaluation of system performance
The present study aims to determine model coefficients using the Angstrom-Prescott formula through regression analysis, two-dimensional principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS) for estimating GSR on horizontal surface
The regression equations, that is, first order, second order and third order AngstromPrescott formula have been used for the estimation of global solar radiation in Akure
Summary
Global solar radiation (GSR) measurements play a fundamental role in the design of solar engineering projects and in evaluation of system performance. Angstrom (1924) proposed the first linear regression model for estimating global solar radiation using sunshine duration data. The present study aims to determine model coefficients using the Angstrom-Prescott formula through regression analysis, two-dimensional PCA and adaptive neuro-fuzzy inference system (ANFIS) for estimating GSR on horizontal surface.
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