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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

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computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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A comparative study between two algorithms for maximum power point tracking Perturb and Observation (P&O) and Sliding Mode Controller (SMC) 1

A comparative study between two algorithms for maximum power...

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1st International Conference on Renewable Solutions for Ecosystems: Towards a sustainable Energy Transition, ICRSEtoSET 2023

作者： Abdelhalim, Taibi Kouider, Laroussi Abdelkader, Rouibah Hartani, Mohamed Amine Djelfa Algeria Ahmed. Draia University Sustainable Development and Computer Science Laboratory SDCS-L Adrar Algeria

ISBN: (纸本)9798350346336

The aim of this paper is to provide a comparative analysis of two maximum power point tracking (MPPT) algorithms, (i) Perturb and Observation (P&O) and (ii) Sliding Mode Controller (SMC). The two algorithms were applied under varying solar conditions. The simulation findings show that the SMC-based MPPT controller can track (MPP) more quickly than the conventional approach of Perturb and Observation technique (P&O) controller under various weather circumstances under different irradiance levels. This achievement is attained through adjusting the duty cycle of the DC-DC Boost converter. The PV peak power can be acquired without any disruption under different environmental circumstances resulting in improved load supply efficiency. To conclude, the MPPT-SMC outperformed the P&O-MPPT enabling superior performance and accurate tracking of maximum available power points. © 2023 IEEE.

关键词： Controllers

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Intelligent Energy Management Strategy For Multi-Sources Isolated DC-Microgrid 2

Intelligent Energy Management Strategy For Multi-Sources Iso...

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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Oussama, Hafsi Amine, Hartani Mohamed Mohamed Amine, Soumeur Aissa, Benhammou Abdeselem, Chakar Othmane, Abdelkhalek Ahmed Draia University Sustainable Development and Computer Science Laboratory Adrar Algeria Tahri Mohamed University Smart Grids and Renewable Energies Laboratory Bechar Algeria

ISBN: (纸本)9798350372182

In recent years, DC microgrids have exhibited notable advantages over traditional counterparts, particularly in standalone and agricultural applications. This paper presents a standalone DC microgrid integrating multiple energy sources, energy storage systems, and power electronic converters. Emphasizing the region's substantial solar irradiation and wind potentials, the primary focus lies on Photovoltaic (PV) and Wind Turbine (WT) systems. Additionally, a Fuel Cell (FC) serves as a secondary power source to ensure energy continuity in critical scenarios. To effectively manage this complex system, an Intelligent Energy Management System (EMS) employing Fuzzy Logic Control (FLC) has been proposed. This FLC regulates the FC system's current reference based on the power discrepancy between the generation and demand sides while considering the battery's State of Charge (SOC). Utilizing the Matlab/Simulink environment, the results demonstrate the FLC's efficiency in managing the entire system, prolonging battery lifespan, and minimizing hydrogen (H2) consumption from the FC source. © 2023 IEEE.

关键词： Renewable energy

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Automated Heartbeat Classification for Arrhythmia Patients Using a Deep Convolutional Neural Network 8

Automated Heartbeat Classification for Arrhythmia Patients U...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kerdoudi, Saoueb Guezouli, Larbi Hattab, Abdessalam Dilekh, Tahar University of Batna 2 LaSTIC Laboratory Computer Science Department Batna Algeria Higher National School of Renewable Energies Environment and Sustainable Development LEREESI Laboratory of Renewable Energy Energy Efficiency and Smart Systems Batna Algeria University of Batna 2 LAMIE Laboratory Computer Science Department Batna Algeria

ISBN: (纸本)9798350309249

The electrocardiogram (ECG) is widely used for diagnosing heart diseases, including arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and classification of arrhythmic types are crucial in preventing heart disease and reducing mortality. To enhance ECG heartbeat classification, we propose an automated system based on a fine-tuned deep convolutional neural network consisting of 13 residual blocks with 28 layers in total. This model was trained using a dataset of 103247 single-lead ECG recordings. Our deep convolutional neural network achieved remarkable results by validating the independent test dataset from the MIT-BIH Arrhythmia Database. It demonstrated an Accuracy of 99%, a Macro avg Precision of 96%, a Macro avg Recall of 93%, a Weighted avg of 99%, and an impressive Macro avg F1-score of 94%. Notably, our approach has demonstrated superior performance to the original architecture, with a 3% increase in F1-score. These findings showcase the potential of our improved deep learning approach to effectively classify a wide range of arrhythmias from single-lead ECGs, yielding high diagnostic performance comparable to that of cardiologists. © 2024 IEEE.

关键词： Electrocardiograms

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Integration of Photovoltaic, Lithium Battery, and Supercapacitor Technologies for Enhanced DC Voltage in Standalone Microgrids 2

Integration of Photovoltaic, Lithium Battery, and Supercapac...

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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Abdelhalim, Taibi Kouider, Laroussi Abdelkader, Rouibah Hartani, Mohamed Amine B.P. 311. Djelfa Djelfa Algeria Ahmed Draia University Sustainable Development and Computer Science Laboratory SDCS-L Adrar Algeria

ISBN: (纸本)9798350372182

In this study, photovoltaic (PV) panels, lithium battery storage systems, and supercapacitors are integrated to enhance the reliability and stability of standalone microgrids. Despite their importance, standalone microgrids face challenges relating to intermittent energy sources and voltage fluctuations in remote and off-grid *** supercapacitors in conjunction with batteries can enhance power delivery and protect battery systems. This study investigates how supercapacitors can enhance energy efficiency, extend battery life, and ensure a robust power supply. The advantages of supercapacitor-battery hybrid systems, as well as mechanisms that ensure seamless energy management and battery protection, are explored in this study. In energy storage and electrification applications, these hybrid systems provide enhanced performance, reliability, and longevity by utilizing supercapacitors. It has been found that voltage stability and energy management have been significantly improved, indicating the potential for sustainable energy solutions in remote locations. © 2023 IEEE.

关键词： Supercapacitor

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Enhanced Vdc Bus Stability for PV and Battery Systems through an Optimized FOPID Controller using a Bidirectional DC-DC Converter 2

Enhanced Vdc Bus Stability for PV and Battery Systems throug...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Abdelhalim, Taibi Kouider, Laroussi Abdelkader, Rouibah Hartani, Mohamed Amine B.P. 311. Djelfa. Djelfa Algeria Draia University Sustainable Development and Computer Science Laboratory SDCS-L Ahmed Adrar Algeria

ISBN: (纸本)9798350314243

In modern power systems, renewable energy sources (RES), such as wind turbine, photovoltaics and (BES) battery energy storage are becoming increasingly important. However, maintaining a stable direct voltage (Vdc) bus poses a challenge for these systems. This paper proposes an effective approach to improve Vdc bus stability by introducing an optimized Fractional-Order Proportional-Integral-Derivative (FOPID) controller, designed using a Genetic Algorithm (GA). Employing an FOPID strategy in the control of a bidirectional converter holds the potential to outperform an optimized PI (Proportional-Integral-Derivative) controller, particularly in terms of curtailing overshoot, bolstering stability. © 2023 IEEE.

关键词： Genetic algorithms

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power Engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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IoV Data Processing Algorithms for Automatic Real-Time Object Detection - Literature Review 6

IoV Data Processing Algorithms for Automatic Real-Time Objec...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Zineb, Seghir Lyamine, Guezouli Kamel, Barka Rezki, Assem Computer Science Department LaSTIC Laboratory Batna 2 University Batna Algeria Renewable Energies and New Technologies Department LEREESI Laboratory Higher National School of Renewable Energies Environment & Sustainable Development Batna Algeria

ISBN: (纸本)9798350398496

With the advent of the Internet of Vehicles (IoV), there is an increasing demand for real-time and accurate object detection algorithms for intelligent transportation systems. This literature review aims to provide a comprehensive analysis of the latest research in IoV data processing algorithms for automatic real-time object detection. By analyzing and comparing various state-of-the-art algorithms such as Faster R-CNN, Mask R-CNN, and YOLOv4, the strengths and limitations of each approach are identified. Additionally, the challenges associated with real-world implementation are discussed and provide recommendations for future research directions. This research study highlights the importance of efficient and accurate object detection algorithms in the context of the IoV and emphasizes the need for ongoing research in this field. © 2023 IEEE.

关键词： Autonomous vehicles

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Real-Time Pedestrian Detection in Hazy Weather for Surveillance Systems 8

Real-Time Pedestrian Detection in Hazy Weather for Surveilla...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Yassine, Bouafia Larbi, Guezouli Lakhlef, Hicham University of Batna2 Algeria LaSTIC Laboratory Computer Science Department Batna Algeria Environment and Sustainable Development LEREESI Laboratory Higher National School of Renewable Energy Batna Algeria Sorbonne University University of Technology of Compiègne Compiègne France

ISBN: (纸本)9798350309249

Real-time pedestrian detection is an expanding research topic that is crucial in various vision-based applications. Effective detection of pedestrians in various environments and situations will significantly improve human safety. However, the performance of current pedestrian detection algorithms faces challenges in scenarios with reduced visibility and blurred appearance, such as hazy weather conditions. This study addresses this issue by seeking an optimal balance between accuracy and speed for deploying deep learning-based pedestrian detection in real-time intelligent surveillance systems. For this aim, we used the transfer learning technique to fine-tune small versions of state-of-the-art YOLO models (YOLONAS-small, YOLOv8-small, YOLOv6-small, and YOLOv5-small) to perform pedestrian detection in hazy weather effectively. Experiment results demonstrate the effectiveness of our methods in accurately detecting pedestrians under hazy conditions by achieving high average precision and F-Score. Moreover, they all operate in real time, with impressive processing speeds ranging from 52.41 to 186.22 FPS. © 2024 IEEE.

关键词： Deep learning

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Data Processing from VANETs to IoV: Literature Review 8th

Data Processing from VANETs to IoV: Literature Review

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8th Future Technologies Conference, FTC 2023

作者： Assem, Rezki Lyamine, Guezouli Abderrezak, Benyahia Zineb, Seghir Abdelkrim, Lamraoui Computer Science Department LaSTIC Laboratory University - Batna 2 Batna Algeria Renewable Energies and New Technologies Department LEREESI Laboratory Higher National School of Renewable Energies Environment & Sustainable Development - Batna Batna Algeria

ISBN: (纸本)9783031474538

The amount of data generated by Vehicular Ad-Hoc Networks (VANETs) and the Internet of Vehicles (IoV) has increased exponentially, providing valuable insights into traffic patterns, road conditions, and driver behavior. However, the unstructured, incomplete, and unreliable nature of the data makes it challenging to process and analyze effectively. Traditional data processing techniques are limited in handling the complexity and scale of the data generated from VANETs and IoV due to the volume, variety, velocity, and veracity of the data. Inadequate data processing can result in inaccurate predictions and decisions, decreased system efficiency, and increased risk of safety incidents. This literature review plays a critical role in advancing our understanding of data processing in VANETs and IoV, identifying challenges and opportunities in this field, and exploring the potential benefits of more effective data processing techniques for the future of smart transportation systems (ITS). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicular ad hoc networks

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