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This paper aims to propose an approach based on physical model integration for surface and cloud albedo computation using an approximate form of the atmospheric radiative transfer equation and sun-pixel-satellite.

The data used in this study are global irradiance collected from for various sites in Algeria, and data were obtained from the processing of the high-resolution visible images taken by the Meteosat Second Generation satellite in 2010.

The results suggest that the standard deviation obtained with this method is similar to that obtained with current estimation methods. The hourly and daily correlation coefficients range between 0.95 and 0.97 and between 0.97 and 0.99, respectively. The hourly and daily mean bias errors range between −0.2 and +1.2 per cent and between −0.2 and +1.4 per cent, respectively. The hourly and daily root mean square errors range between 10 and 17 per cent and between 4 and 8 per cent, respectively.

This paper developed a new estimating method that derives the hourly global horizontal solar irradiation at a ground level from geostationary satellite data under local climate conditions.

This study aims to estimate the electric power production of the 20 MWp solar photovoltaic (PV) plant installed in the Adrar region, South of Algeria using minimal knowledge about weather conditions.

In this study, simulation models based on linear and nonlinear approaches were used to estimate accurate energy production from minimum radiometric and meteorological data. Simulations have been carried out by using multiple linear regression (MLR) and artificial neural network (ANN) models with three basic types of neuron connection architectures, namely, feed-forward neural network, cascade-forward neural network (CNN) and Elman neural network. The performance is measured based on evaluation indexes, namely, mean absolute percentage error, normalized mean absolute error and normalized root mean square error.

A comparison of the proposed ANN models has been made with MLR models. The performance analysis indicates that all the ANN-based models are superior in prediction accuracy and stability, and among these models, the most accurate results are obtained with the use of CNN-based models.

The considered model will be adopted in solar PV forecasting areas as part of the operational forecasting chain based on numerical weather prediction. It can be an effective and powerful forecasting approach for solar power generation for large-scale PV plants.

The operational forecasting system can be used to generate an effective schedule for national grid electricity system operators to ensure the sustainability as well as favourable trading performance in the electricity, such as adjusting the scheduling plan, ensuring power quality, reducing depletion of fossil fuel resources and consequently decreasing the environmental pollution.

The proposed method uses the instantaneous radiometric and meteorological data in 15-min time interval recorded over the two years of operation, which made the result exploits a fact that the energy production estimation of PV power generation station is comparatively more accurate.

Integrated solar combined cycle (ISCC) using parabolic trough collector (PTC) technology is a new power plant that has been installed in few countries to benefit from the use of hybrid solar-gas systems. The purpose of this paper is to investigate the challenges in modeling the thermal output of the hybrid solar-gas power plant and to analyze the factors that influence them.

To validate the proposal, a study was conducted on a test stand in situ and based on the statistical analysis of meteorological data of the year 2017. Such data have been brought from Abener hybrid solar-gas central of Hassi R’mel and used as an input of our model.

The proposal made by the authors has been simulated using MATLAB environment. The simulation results show that the net solar electricity reaches 18 per cent in June, 15 per cent in March and September, while it cannot exceed 8 per cent in December. Moreover, it shows that the power plant responses sensibly to solar energy, where the electricity output increases accordingly to the solar radiation increase. This increase in efficiency results in better economic utilization of the solar PTC equipment in such kind of hybrid solar-gas power plant.

The obtained results would be expected to provide the possibility for designing other power plants in Algeria when such conditions are met (high DNI, low wind speed, water and natural-gas availability).

This paper presents a new model able to predict the thermal solar energy and the net solar-electricity efficiency of such kind solar hybrid power plant.

Ballast water of merchant ship is a source of introduction of invasive species around the globe. The purpose of this paper is to present a quantitative risk assessment applied to a model port, the Port of Arzew in Algeria, and based on an analysis of this port’s shipping traffic.

The risk assessment for introduction of invasive species is interpreted in the form of a probabilistic process, with a combination of two probabilities. The first probability is related to the ability of a species to arrive to the destination (recipient port), depending on the quantity of water ballast discharged and the duration of voyage. The second one is based on the species ability to survive in their new environment, which depends on the environmental similarity between donor port and Arzew port.

This assessment’s outcome consists on a classification of scenarios regarding their acceptability. Consequently, it helped to classify donor ports according to a risk scale, from low risk to high-risk donor ports.

The phenomenon of invasion of aquatic species is a complex process. Factors such as adaptation and tolerance of species, the attendance or absence of predators, were not taken into account in this study.

This study could be used by the maritime administration as a decision-making tool regarding the issue of exemptions under the IMO International Convention on the Management of Ballast Water and Sediments 2004.

This is one of the first known studies in Algeria and dealing with ballast water management. The results of this assessment provide useful information to policy makers, in order to develop a national strategy to reduce the impact of shipping pollution on the marine environment.