<abstract xmlns="http://www.w3.org/1999/xhtml">The need for adequate solar radiation is ever increasing for various applications. However there is an inadequate data of solar radiation in many countries due to the cost of instrument set up. Hence this study investigates two models for estimating solar radiation from routinely measured meteorological parameters. The data were obtained from the International Institute of Tropical Agriculture, Ibadan. The regression coefficients of the quadratic models were determined and used to estimate the global solar radiation for both forward and backward predictions. Their predictive accuracies were compared with four other models and the measured values using standard statistical error indicators. The results showed for forward as compared to backward predictions in bracket root mean square errors 1.2 (1.1); mean bias errors 1.1 (0.8) and mean percentage errors -4.8% (-2.9%) while for backward prediction 1.9 (1.7), 1.7 (1.4) and 7.9% (2.2%) measured in KJm−2day−1 respectively. A positive error value shows an over estimation while a negative value shows an under estimation. The models are versatile for estimating global solar radiation at the horizontal surface, fixing missing data and correcting outliers.</abstract>

The need for adequate solar radiation is ever increasing for various applications. However there is an inadequate data of solar radiation in many countries due to the cost of instrument set up. Hence this study investigates two models for estimating solar radiation from routinely measured meteorological parameters. The data were obtained from the International Institute of Tropical Agriculture, Ibadan. The regression coefficients of the quadratic models were determined and used to estimate the global solar radiation for both forward and backward predictions. Their predictive accuracies were compared with four other models and the measured values using standard statistical error indicators. The results showed for forward as compared to backward predictions in bracket root mean square errors 1.2 (1.1); mean bias errors 1.1 (0.8) and mean percentage errors -4.8% (-2.9%) while for backward prediction 1.9 (1.7), 1.7 (1.4) and 7.9% (2.2%) measured in KJm−2day−1 respectively. A positive error value shows an over estimation while a negative value shows an under estimation. The models are versatile for estimating global solar radiation at the horizontal surface, fixing missing data and correcting outliers.

Estimating Global Solar Radiation from Routine Meteorological Parameters Over a Tropical City (7.23°N; 3.52°E) Using Quadratic Models

Department of Mathematical and Physical Sciences

Annals of West University of Timisoara - Physics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The need for adequate solar radiation is ever increasing for various applications. However there is an inadequate data of solar radiation in many countries due...

Estimating Global Solar Radiation from Routine Meteorological Parameters Over a Tropical City (7.23°N; 3.52°E) Using Quadratic Models

Pubblicato online: 29 ago 2018

Pagine: 45 - 55

Ricevuto: 31 mag 2017

Accettato: 30 ott 2017

DOI: https://doi.org/10.2478/awutp-2018-0005

Parole chiave
Solar-radiation, Sunshine-hour, Minimum and maximum temperatures, Measured, Estimated

© 2018 Gbadebo I. Olatona, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Estimating Global Solar Radiation from Routine Meteorological Parameters Over a Tropical City (7.23°N; 3.52°E) Using Quadratic Models

Pubblicato online: 29 ago 2018

Pagine: 45 - 55

Ricevuto: 31 mag 2017

Accettato: 30 ott 2017

DOI: https://doi.org/10.2478/awutp-2018-0005

Parole chiaveSolar-radiation, Sunshine-hour, Minimum and maximum temperatures, Measured, Estimated

© 2018 Gbadebo I. Olatona, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Solar-radiation, Sunshine-hour, Minimum and maximum temperatures, Measured, Estimated