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Machine Learning Forecasting Approaches for Evapotranspiration: A Comparative Analysis 4th

Machine Learning Forecasting Approaches for Evapotranspirati...

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4th International Conference on Electronic Engineering and renewable energy systems, ICEERE 2024

作者： Mokhtari, Hassan Benzaouia, Mohammed Hajji, Bekkay Ayadi, Nabil Chaabane, Khalid Faculty of Sciences Mohamed First University Oujda Morocco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco

ISBN: (纸本)9789819606436

Accurate prediction of reference evapotranspiration (ET0) is essential for effective management across various sectors, particularly in agriculture and irrigation. This paper presents a comparative analysis of three prominent machine-learning techniques, Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing a comprehensive dataset of meteorological variables. The study focuses on the eastern region of Morocco, specifically Oujda, where water resource management is of critical importance. Through rigorous experimentation and evaluation, our findings reveal SVM’s superior accuracy (R2 = 0.9981) in ET0 prediction when compared to RF and KNN. This superior performance underscores SVM's potential for precise ET0 forecasting, facilitating informed decision-making in water resource management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Efficient Fuzzy Logic MPPT Controls for Sudden Change in Load 3rd

Efficient Fuzzy Logic MPPT Controls for Sudden Change in Loa...

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3rd International Conference on Electronic Engineering and renewable energy, ICEERE 2022

作者： Boutaybi, Mohammed Khlifi, Yamina Bekkay, Hajji Energy Embedded System and Data Processing Laboratory ENSAO Mohamed First University Oujda Morocco

ISBN: (纸本)9789811962226

The objective of this paper is to study the performance of a photovoltaic (PV) system controlled by two types of Maximum Power Point Tracking (MPPT) commands under a rapid change in load. These commands are, respectively, perturb and observe (P &O) and improved fuzzy logic (FL) based on two types of algorithms: Mamdani and Takagi-Sugeno (TS). These improved FL commands are based on two inputs which represent the slope of the power-current curve and changes of this slope instead of the power-voltage curve slope. The designed PV system consists of a typical PV panel (1Soltech 1STH215-P) feeding a resistive load via a (DC/DC) converter where the MOSFET is controlled by PWM signal generated by the MPPT control. The simulation results obtained using MATLAB/Simulink environment show that the performance of the boost converter is very satisfactory (97%) in efficiency and that FL-Mamdani, and FL-TS MPPTs controls are more efficient than the classical P &O. Indeed, the comparison of different performance parameters, such as PV system efficiency and response time to achieve MPP, shows that the PV system efficiency obtained by fuzzy logic method is about 99.60% for FL-Mamdani, 99.64% for FL-TS and 99.20% for P &O method under the rapid change in load. In addition, the response time is shorter for FL methods (25.7 ms for FL-Mamdani and 21.9 ms for FL-TS) than in P &O method which records 31.5 ms. Furthermore, the proposed FL controls (FL-Mamdani and FL-TS) reduce the oscillations obtained by the P &O method and converge quickly to the maximum power point (MPP) regardless the load change. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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Neural Network-Based Precision Irrigation Scheduling and Crop Water Stress Index (CWSI) Assessment 3rd

Neural Network-Based Precision Irrigation Scheduling and Cro...

引用

3rd International Conference on Electronic Engineering and renewable energy, ICEERE 2022

作者： Mohammed, Benzaouia Bekkay, Hajji Hassan, Mokhtari Khalid, Chaabane Embedded System and Data Processing Laboratory Renewable Energy National School of Applied Sciences Mohamed First University Oujda Morocco Faculty of Sciences Oujda Mohamed First University Oujda Morocco

ISBN: (纸本)9789811962226

The efficient use of water by irrigation systems is critical for sustainable agricultural development, food security, and overall economic growth. This is especially true in light of global population growth, climate change, and competitive water demands from other economic sectors. To meet this challenge, developing a precision irrigation (PI ) strategy is targeted. The developed technique consists of applying the exact amount of water needed by the crop according to its phonological stage through assessing the crop water stress index (CWSI), climatic conditions, and soil water stock. The strategy is based on a neural network to improve the system’s behavior and render it intelligent and automatic. The obtained results lead to increased efficiency, saving irrigation water, and consequently, increasing water productivity. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

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A Review on Precision Irrigation Techniques and Controls for Better Water Use Efficiency in Agriculture 3rd

A Review on Precision Irrigation Techniques and Controls for...

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3rd International Conference on Electronic Engineering and renewable energy, ICEERE 2022

作者： Mokhtari, Hassan Benzaouia, Mohammed Bekkay, Hajji Chaabane, Khalid Faculty of Sciences Oujda Mohamed First University Oujda Morocco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco

ISBN: (纸本)9789811962226

The rapid increase in demand for freshwater due to population growth and the emergence of different industrial sectors, specifically the agricultural sector, consumes huge quantities of water, while the effects of global warming pose a serious threat to food security and water availability. To achieve this, irrigation system development is needed to reduce and streamline both water and energy use. In addition, the automation orientation of these systems can improve productivity, increase irrigation efficiency, and reduce labor costs. This paper presents in-depth research on traditional irrigation techniques and techniques recently developed by researchers over the last few years. This paper is expected to be used to improve researchers’ knowledge and provide in-depth knowledge related to irrigated agriculture. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global warming

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Improved Control Technique Based on Neural Network for AC-Chopper of Railway Substations 2

Improved Control Technique Based on Neural Network for AC-Ch...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022

作者： Mir, Ismail Anas, Benslimane Ayat, Yassine Mohammed, Benzaouia Mohamed First University Energy Embedded System and Data Processing Laboratory Ensao Oujda Morocco Embedded System and Data Processing Oujda Energy Embedded System and Data Processing Energy Morocco

ISBN: (纸本)9781665422093

Improving the efficiency of the unbalance compen-sators in high-speed railway (HSR), mainly depends on the development of an intelligent control to limit the unbalance factors imposed by the Moroccan energy provider (ONEE). In this paper, a neural network model (ANN) to control the AC-chopper converter has been proposed. The aim is to achieve a stable operation under real conditions of variation of power consumption by substation. The efficiency of this control mode compared to a control method based on look-up table (LUT) with PID corrector is presented. The different stages of the system as well as the control strategy have been modeled and implemented in MATLAB/Simulink. © 2022 IEEE.

关键词： Neural network models

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Improving Power Consumption Efficiency and Cost Reduction Using Firefly Algorithm in Smart Home: A Multi-scenario Analysis 4th

Improving Power Consumption Efficiency and Cost Reduction Us...

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4th International Conference on Electronic Engineering and renewable energy systems, ICEERE 2024

作者： Dhriyyef, Mohammed El Mehdi, Abdelmalek Benzaouia, Mohammed Hebal, Sara Smart ICT Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco LRSD Laboratory Computer Science Department Ferhat ABBAS Sétif 1 Sétif Algeria

ISBN: (纸本)9789819799749

This paper proposes a multi-scenario analysis based on the Firefly Algorithm (FA) to optimize power consumption efficiency and electricity cost reduction in smart homes. It integrates demand side management (DSM), in particular demand response (DR), to encourage consumers to adjust their energy consumption during peak hours. A home energy management system (HEMS) is developed, optimizing appliance scheduling and air-conditioning system control while minimizing electricity costs and ensuring thermal comfort. Moreover, appliance comfort. Based on three scenarios reflecting different user priorities, significant reductions in electricity costs are achieved, ranging from 6.42 to 21%, while maintaining user-defined comfort levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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Enhanced Fault Classification in Photovoltaic Panels Using Random Forest and k-Nearest Neighbors 9th

Enhanced Fault Classification in Photovoltaic Panels Using R...

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9th International Conference on Smart City Applications, SCA 2024

作者： Khlifi, Abdelilah Khlifi, Yamina Energy Embedded System and Data Processing Laboratory ENSAO Mohammed First University Oujda Morocco

ISBN: (纸本)9783031886522

Photovoltaic (PV) panels can encounter various issues due to an aggressive working environment and malfunctions caused by environmental or human factors. These issues, known as faults, can significantly reduce PV system performance and increase the risk of fire. To address this, numerous diagnostic methods have been developed, with fault classification being a principal part of diagnosing faults in PV panels. This article proposes a fault classification approach using two machine learning algorithms, with I(V) characteristics as input parameters. These algorithms are designed to identify three specific faults in the series-parallel configuration of solar panels: partial shading, bypass diode short circuits, and ground fault. These faults are commonly found in photovoltaic fields. data preprocessing techniques are employed to enhance the quality of the fault dataset. A comparison is made between two algorithms, considering factors such as accuracy, Standard deviation of scores, root mean square error (RMSE). Experimental results show that the diagnostic algorithm based on random forest (RF) exhibits higher precision and generalization capability compared to k-Nearest Neighbors (kNN) for classifying faults in photovoltaic modules within a solar field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Random forests

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Control and Performance Assessment of Fuel Cell/Supercapacitor Hybrid systems 4th

Control and Performance Assessment of Fuel Cell/Supercapacit...

引用

4th International Conference on Electronic Engineering and renewable energy systems, ICEERE 2024

作者： Benzaouia, S. Rabhi, Abdelhamid Brahim, K. Sidi Benzaouia, M. Elgharib, A.O. MIS Laboratory University of Picardie Jules Verne Amiens Cedex France Embedded System and Data Processing Laboratory Renewable Energy National School of Applied Sciences Mohamed First University Oujda Morocco Arab Academy for Science Technology and Maritime Transport Giza Egypt

ISBN: (纸本)9789819606436

This paper presents the control of a fully active Fuel Cell (FC)—Supercapacitor (SC) hybrid system using sliding mode control (SMC) theory. The suggested SMC technique is developed based on the system’s nonlinear model, utilizing Lyapunov stability design techniques. The proposed control technique enables tight regulation of the DC bus voltage, high tracking performance of the SC and FC current to their references and ensures asymptotic stability of the closed-loop system. Numerical simulations are provided to demonstrate that the proposed technique achieves all desired objectives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sliding mode control

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Modeling and Analysis of a Fuel Cell-Battery Hybrid Electric Vehicle 3rd

Modeling and Analysis of a Fuel Cell-Battery Hybrid Elect...

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3rd International Conference on Electronic Engineering and renewable energy, ICEERE 2022

作者： Essoufi, M. Bekkay, Hajji Rabhi, Abdelhamid Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Modelization Information and Systems Laboratory Picardie Jules Verne University Amiens France

ISBN: (纸本)9789811962226

In recent years, the lack of energy and the emission of greenhouse gases has led the research community to devote time and effort;to develop solutions for the new generation of vehicles. In this context, modeling and numerical simulation bring real added value because they reduce the cost and time of development. This article deals with the modeling and performance analysis of a fuel cell-battery hybrid electric vehicle (FCHEV). The power supply system consists of a fuel cell as a primary source and a battery as a secondary one. The various topologies are analyzed, and the commands used to control the current and voltage of the DC bus are presented. The performance of the proposed FCHEV model is verified by numerical simulations for the Urban Dynamometer Driving Schedule (UDDS), using MATLAB Simulink, considering a rule-based energy management strategy. Finally, the results show the performance and efficiency of the drive system under different operating modes of the FCHEV over a complete cycle: acceleration, deceleration, battery charge during acceleration, and regenerative braking. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuel cells

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Frequency Splitting-Based High Order Sliding Mode Control Strategy for Electric Vehicle 4th

Frequency Splitting-Based High Order Sliding Mode Control St...

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4th International Conference on Electronic Engineering and renewable energy systems, ICEERE 2024

作者： Benzaouia, Mohammed Essoufi, Mohammed Hajji, Bekkay Rabhi, Abdelhamid Embedded System and Data Processing Laboratory Renewable Energy National School of Applied Sciences Mohamed First University Oujda Morocco Laboratory of Modelization Information and Systems University of Picardie Jules Verne 3 Rue Saint Leu Amiens Cedex 180039 France

ISBN: (纸本)9789819606436

Hydrogen-powered electric vehicles (EVs) represent a significant advancement in clean transportation, utilizing innovative energy production and storage solutions. This paper proposes a robust control strategy for such systems, employing a combination of frequency splitting and the super-twisting algorithm (STA). The investigated system comprises a fuel cell (FC) integrated with a hybrid energy storage system (HESS) that utilizes both batteries and supercapacitors (SC). The DC-DC boost converter optimizes FC power output, while bidirectional DC-DC converters facilitate seamless power flow within the HESS for charging and discharging. This control strategy aims to overcome limitations inherent to classical PID control methods. Numerical simulations conducted in Matlab/Simulink validate the proposed approach's efficacy in mitigating chattering and over and undershoot phenomena under dynamic load conditions. The results conclusively demonstrate the effectiveness of the control strategy in enhancing overall system performance, enabling well-regulated power exchange, and achieving improved efficiency, reduced power losses, and enhanced system stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hydrogen storage

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