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Title | Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction |
Authors |
Mbah, O.M.
Madueke, C.I. Umunakwe, R. Okofor, C.O. |
ORCID | |
Keywords |
machine learning sky models solar energy solar radiation tilted surface |
Type | Article |
Date of Issue | 2022 |
URI | https://essuir.sumdu.edu.ua/handle/123456789/89198 |
Publisher | Sumy State University |
License | Creative Commons Attribution 4.0 International License |
Citation | Mbah, O. M., Madueke, C. I., Umunakwe, R., Okafor, C.O. (2022). Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction. Journal of Engineering Sciences, Vol. 9(2), pp. G1-G6, doi: 10.21272/jes.2022.9(2).g1 |
Abstract |
In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest
Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate
global solar radiation on daily data measured for one year period at the National Center for Energy, Research and
Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar
radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured
global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were
used as input for the prediction process. Python computational software was used for model prediction, and the
performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error
(MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the
Extreme Gradient Boosting (XGBoost) algorithm offered the best performance with the least inaccuracy to sky models. |
Appears in Collections: |
Journal of Engineering Sciences / Журнал інженерних наук |
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