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

Solar energy represents one of themost 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 ARRF models outperform all other hybrid models.The provided models were validated by statisticalmetrics,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.Hence,this study recommends new effective methods to predict GSR in semiarid regions.

Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable.The photovoltaic(PV)mechanism produces sunbeams’green energy without noise or pollution.The PV mechanism seems simple,seldom malfunctioning,and easy to install.PV energy productivity significantly contributes to smart grids through many small PV mechanisms.Precise solar radiation(SR)prediction could substantially reduce the impact and cost relating to the advancement of solar energy.In recent times,several SR predictive mechanism was formulated,namely artificial neural network(ANN),autoregressive moving average,and support vector machine(SVM).Therefore,this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction(OMBGRU-SRP)for energy management.The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process.To accomplish this,the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data.Next,the MBGRU model is derived using BGRU with an attention mechanism and skip connections.At last,the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization(SBO)algorithm to attain maximum prediction with minimum error values.The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild.Many experiments were conducted to demonstrate the enhanced SR prediction performance.The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.