Simulation and Experimental Studies of Illumination Effects on the Current Transport of Nitridated GaAs Schottky Diode

Semiconductors - In this paper, we studied the electrical characteristic of Schottky diodes based on gold contact on nitridated GaAs substrates. The used (100) GaAs substrate is n-type with...     

Decomposing global solar radiation into its diffuse and direct normal radiation

ABSTRACT

This work presents a model based on Radial Basis Function (RBF) to estimate the diffused solar radiation (DSR) and direct normal radiation (DNR) fractions of solar radiation from global solar radiation in a semiarid area in Algeria based on a database measured between 2013 and 2015. The data has been collected at Applied Research Unit for Renewable Energies, (URAER) at Ghardaia city situated in the south of Algeria. The experimental results show that RBF model estimates DNR and DSR with high performance. The difference between the measured and the predicted values show a normalised Root Mean Square Error (nRMSE) of 0.033 and 0.065 for DNR and DSR, respectively. The obtained values of Determination Coefficient (R²) and Correlation Coefficient (R) are: 97.3%, 98.60%, respectively for DNR and 88.89%, 91.12% For DSR.

However, the obtained results are very plausible and showed that RBF model estimates the DSR and DNR with good accuracy.     

Soft computing for solar radiation potential assessment in Algeria

ABSTRACT

Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013–2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.     

Multi-step-ahead forecasting of daily solar radiation components in the Saharan climate

ABSTRACT

Accurate estimation of renewable energy sources plays an important role in their integration into the grid. An unexpected atmospheric change can produce a range of problems related to various solar plant components affecting the electricity generation system. Global solar radiation (GSR) assessment has been increased in the past decade due to its important use in photovoltaic application. In this paper, we propose the use of machine learning-based models for daily global and direct solar radiation forecasting in a semi-arid climate, using a combination set of meteorological parameters on a horizontal surface in the Ghardaïa region. The models are presented and implemented on 3-year measured meteorological data at Applied Research Unit for Renewable Energies (URAER) at Ghardaïa city between 2014 and 2016. The results show that both MLP and RBF models perform well for three-step-ahead forecasting with a slight improvement in MLP models in terms of statistical metrics.