Journal of Renewable Energies <p> </p> <p> </p> <center><strong>The Journal of Renewable Energies (Revue des Energies Renouvelables)</strong></center> <p> </p> <div style="width: 100%; padding: auto; height: auto;"><center><img src="" alt="" width="310" height="400" /><br /><strong>ISSN: 1112-2242</strong><br /><strong>EISSN: 2716-8247</strong></center><center></center></div> <div style="width: 100%; padding: 10px; height: auto;"> <p> </p> <p><strong>Description</strong></p> <p>The Journal of Renewable Energies (Revue des Energies Renouvelables) is an international peer-reviewed journal published by the Renewable Energy Development Center (CDER). The journal was founded in 1998 to promote research and dissemination of knowledge on renewable energy. The Journal of Renewable Energies covers a wide range of topics that include but not limited to solar, wind, geothermal, biomass energy, hydrogen, and the environment. Particular attention is paid to energy analysis and modelling, energy conservation and storage, energy efficiency, energy demand and supply. The journal also welcomes papers on studies with an interaction between renewable energies and other scientific fields such as thermodynamics, mechanics, electricity, chemistry, biology, materials science and the protection of the environment.</p> <p> </p> <p><strong>Editor-in-chief</strong></p> <div class="name">Amar HADJ ARAB, Director of Research</div> <div class="affiliation">Renewable Energy Development Center (CDER), Algiers, Algeria</div> <div class="email"><a href="" rel="noreferrer"></a></div> <p> </p> <p><strong>Support Contact</strong></p> <p>Mohamed DEBBACHE, Senior researcher.</p> <p>Renewable Energy Development Center (CDER), Algiers, Algeria</p> <p><a href=""></a></p> <p> </p> <p><strong>Secretariat</strong></p> <p>Rafik HALALCHI</p> <p>Renewable Energy Development Center (CDER), Algiers, Algeria</p> <p><a href=""></a></p> </div> Renewable Energy Development Center en-US Journal of Renewable Energies 1112-2242 <div id="deed-conditions" class="row"> <ul class="license-properties col-md-offset-2 col-md-8" dir="ltr"> <li class="license by"> <p><strong>Attribution</strong> — You must give <a id="appropriate_credit_popup" class="helpLink" tabindex="0" title="" href="" data-original-title="">appropriate credit</a>, provide a link to the license, and <a id="indicate_changes_popup" class="helpLink" tabindex="0" title="" href="" data-original-title="">indicate if changes were made</a>. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<span id="by-more-container"></span></p> </li> <li class="license sa"> <p><strong>ShareAlike</strong> — If you remix, transform, or build upon the material, you must distribute your contributions under the <a id="same_license_popup" class="helpLink" tabindex="0" title="" href="" data-original-title="">same license</a> as the original.<span id="sa-more-container"></span></p> </li> </ul> </div> <div class="row"> <ul id="deed-conditions-no-icons" class="col-md-offset-2 col-md-8"> <li class="license"><strong>No additional restrictions</strong> — You may not apply legal terms or <a id="technological_measures_popup" class="helpLink" tabindex="0" title="" href="" data-original-title="">technological measures</a> that legally restrict others from doing anything the license permits.</li> </ul> </div> Artificial neural network-based modeling for the prediction of heat and mass transfer coefficient of the adiabatic liquid desiccant system <p align="justify">This work, based on the data obtained from the literature reported by (Varela et al., 2018), aims to use the artificial neural network approach to predict the heat and mass transfer in a dehumidifier system, using lithium chloride as a liquid desiccant. A neural network model was developed in MATLAB environment based on multilayer perceptron that included an input, hidden, and output layer. The network input parameters are air velocity, air temperature, air humidity ratio, liquid desiccant temperature, liquid flow rate, and liquid desiccant concentration. The network output includes two variables which are the heat transfer coefficient (Kh) and mass transfer coefficient (Km). The performance of the ANN model was evaluated using the statistical parameters between the prediction results and experimental values. The performance regression yields R2 and MSE values of 0.9344 and 9.0032, respectively, for the test data set of heat transfer coefficient (Kh). Moreover, for the mass transfer coefficient (Km), the regression parameter R2 and MSE values for the ANN tests were found to be 0.9657 and 2.0414, respectively. In addition, air velocity, air temperature, solution mass flow rate, and solution concentration are the most influential parameters on the heat and mass transfer between the air and liquid desiccant.</p> Fatih Bouzeffour Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 157 – 167 157 – 167 10.54966/jreen.v25i2.1079 Wind data modeling and energy mapping of the wind potential in the city of Douala (Cameroun) <p align="justify">This work helps in the implementation of wind energy projects in the city of Douala. Wind data used (speed and direction) were collected from January 1, 2020, to December 31, 2020, at a height of 10m. The use of wind speed distribution laws allowed us to obtain predictions of the available wind energy on-site at different altitudes. Then, the wind direction is established for the orientation of the wind turbines and the turbulence analysis is done to highlight the exploitable wind periods. The results show that harnessing wind energy is possible on site from 10 <em>AM</em> to 06 <em>PM</em>. The wind potential at 138 <em>m</em> height has an average speed of 8.15 <em>m/s</em> for an overall energy density of 749.78 W/m<sup>2</sup> for roughness class 0, and an average speed of 3.7 m/s for an overall energy density of 69.118 W/m<sup>2</sup> for roughness class 4. The wind turbines will be installed in front of the north-north-east direction between 15 and 25°. Finally, the energy mapping of the city's wind resources allows us to estimate the total energy available at a reference height.</p> Yvan Martino Dantse Samuel Eke Endouche Kouokam Cyrille Mezoue Jean – Luc Nsouandele Ruben Mouangue Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 169 – 186 169 – 186 10.54966/jreen.v25i2.1080 Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System <p align="justify">This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.</p> Nadjiha Hadjidj Meriem Benbrahim Djamel Ounnas Leila Hayet Mouss Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 187 – 198 187 – 198 10.54966/jreen.v25i2.1081 Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems <p align="justify">The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring.</p> Hichem Hafdaoui El Amin Kouadri Boudjelthia Salim Bouchakour Nasreddine Belhaouas Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 199 – 210 199 – 210 10.54966/jreen.v25i2.1082 Experimental and CFD investigation of cavitation phenomenon in the distributor of a Banki-Michell Turbine <p align="justify">Cavitation is a physical phenomenon that often occurs in hydraulic machines such as pumps, valves, and turbines. Although the Banki-Michell turbine has been used for a long time in small hydropower, no study related to this phenomenon of cavitation in the injector of this turbine has been done. In this study, we will present the results of a numerical study carried out in the nozzle of a Banki-Michell turbine. The numerical solution of the Navier Stokes cavitation equations of the Banki-Michell turbine injector was carried out considering a 2D geometry of the injector-rotor assembly. The simulation results showed that the cavitation phenomenon appears when the water flow area in the nozzle becomes less than 50%. Furthermore, the results also showed that the occurrence of this cavitation phenomenon in the injector is more likely at higher operating heads. The results of an experimental study of the geometry of the injector showed that the height of the water passage section varies linearly with the degree of opening of the stator valve.</p> Jean Bosco Niyonzima Patrick Hendrick Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 211 – 227 211 – 227 10.54966/jreen.v25i2.1083 Etude numérique de l’effet des générateurs de vortex longitudinaux sur le transfert thermique d’un écoulement laminaire traversant un micro-canal <p align="justify">We conducted, in this work, a three-dimensional numerical study of forced convection heat transfer of a laminar flow of water passing through micro-channels with and without longitudinal vortex generator, using the CFD code “Ansys- Fluent”. This work aimed to elucidate the effect of vortex generators on the dynamic and thermal behavior of the micro-fluidic flow. The results obtained show that the increase in the Reynolds number leads to an improvement in the quality of heat transfer in both cases of the study. Rectangular micro-channel with LVG can improve heat transfer compared to smooth rectangular micro-channel while consuming more pressure drop. In the range of Reynolds numbers between 200 and 1200, a 2-21% increase in the mean Nusselt number was observed for micro-channels with LVG compared to smooth micro-channels. This occurs through better mixing of the fluid, reduction in the thickness of the thermal boundary layer, and increased heat transfer area. In addition, the friction factor has been increased by more than 50% compared to smooth micro-channels, due to the local resistance of LVGs and the presence of secondary flows.</p> Azeddine Soudani Zoubir Belkacemi Imane Rahmoune Copyright (c) 2022 Journal of Renewable Energies 2022-12-26 2022-12-26 25 2 229 – 250 229 – 250 10.54966/jreen.v25i2.1084