Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Abstract

Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968-0.988 and RMSE = 1.274-1.403 MJ/m 2 •d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

“…The performance criteria employed are the mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe coefficient (NSE), and correlation coefficient (R). These metrics are computed according to equations (12)–(15 ), as detailed in the referenced studies [ [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] ]. where Q obs , i and Q sim , i are the observed and simulated data, respectively; N denotes the size of the time series, and and are the means of the measured and simulated data, respectively.…”

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

“…These measures are mathematically represented by Eqs. ( 8)- (10) as described in [22][23][24][25][26][27][28][29][30][31][32]. These error measures allow for a comprehensive evaluation of the performance of the prediction models, providing a clear understanding of their strengths and weaknesses.…”