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Controlling the random nature of renewable energy sources such as solar radiation at ground, allows electric grid operators to better integrate it. In this paper, an intelligent datadriven model based on artificial neural network with autoregressive input sequence is developed to forecast the global solar radiation (GSR) time series on a half hour resolution in the site of Agdal, Marrakesh, Morocco. The database that is used to create this model was divided into two subsets. The first subset is used for training the proposed model on the data measured during the year 2008 by adopting three efficient optimizers (levenbergmarquardt, resilient backpropagation, and scaled conjugate gradient). The second subset is used for testing the efficiency and the robustness of the developed model to generate accurate predictions during the next six years (from 2009 to 2014). The obtained results demonstrate the accuracy and the stability of the proposed data-driven model to perform prediction in case of GSR measurements intermittence or sensor damage.

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Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.

A compact wide-band planar antenna is proposed using the metamaterial transmission line. The motivation of this study is to overcome the problem of low bandwidth while designing a miniaturized antenna. The proposed design consists of a rectangular patch and a squareshaped split-ring resonator (SSSRR) on the asymmetric ground plane. Strip lines are provided to connect the right ground plane to obtain the shunt inductances and improve the bandwidth. This antenna has a miniature physical size of 20×28×1.6 mm 3 corresponding to the electrical size of 0.16λo × 0.23λo × 0.013λo, where λo is the free space wavelength at the resonant frequency 2.51 GHz. The obtained results show that this antenna has wide-band from 2.2 GHz to 12.63 GHz, which makes it a good candidate for cover various standards of WLAN (2.4/5.2/5.8 GHz), WiMAX (2.5/3.5/5.5 GHz), satellite TV (7.14 GHz), and X-band (10.19/ 12.02 GHz). The proposed antenna simulations and analyses were performed using the CST simulator.