2 research outputs found
Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction
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 15Β° 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
Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces
Enhancing solar irradiance and accurate forecasting is required for improved performance of
photovoltaic and solar thermal systems. In this study, Extreme Gradient Boosting (XGBoost) model was developed
using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily
global solar radiation on tilted surfaces. The proposed model was built using XGBRegressor with five generations,
100 n estimators, and a learning rate of 0.1. Three statistical metrics, such as the coefficient of determination (R2
),
root mean square error (RMSE), and mean absolute error (MAE), were used to compare the modelβs results to
observed solar radiation data from the Nation Centre for Energy, Research and Development, University of Nigeria,
Nsukka. The results showed improved prediction accuracy and XGBoost capability to estimate daily global solar
radiation on tilted surfaces. In the training section, the proposed model had a statistical performance of R2 = 0.9977,
RMSE = 1.6988, and MAE = 1.081, and in the testing section, R2 = 0.9934, RMSE = 2.8558, and MAE = 2.033.
XGBoost model demonstrated a better performance when compared with other models in the literature. As a result,
the proposed model provides an effective approach for estimating solar radiation