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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

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Clean Energy 2021年第2期5卷 316-328页

作者： Khaled Ferkous Farouk Chellali Abdalah Kouzou Belgacem Bekkar LAADI Laboratory Department of Electrical EngineeringZian Achour UniversityDjelfaAlgeria university of ghardaia GhardaiaAlgeria

Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation,such as artificial intelligence and hybrid ***,the Gaussian process regression(GPR)algorithm has been used successfully in remote sensing and Earth *** this paper,a wavelet-coupled Gaussian process regression(W-GPR)model was proposed to predict the daily solar radiation received on a horizontal surface in ghardaia(Algeria).For this purpose,3 years of data(2013-15)have been used in model training while the data of 2016 were used to validate the *** this work,different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature,relative humidity and extraterrestrial solar radiation on a horizontal *** results demonstrated the effectiveness of the new hybrid W-GPR model compared with the classical GPR model in terms of root mean square error(RMSE),relative root mean square error(rRMSE),mean absolute error(MAE)and determination coefficient(R^(2)).

关键词： Gaussian process regression wavelets hybrid models forecasting solar radiation solar measurements ghardaia

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Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking

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International Journal of Automation and computing 2021年第5期18卷 766-786页

作者： Nacer Hacene Boubekeur Mendil Laboratory of Industrial Technology and Information(LITI) Faculty of TechnologyUniversity of BejaiaBejaia 06000Algeria Department of Automatics and Electromechanics Faculty of Science and TechnologyUniversity of GhardaiaGhardaia 47000Algeria

While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.

关键词： Behavior-based autonomous navigation dynamic obstacles and walls dynamic target tracking formation control of multi-robot systems dynamic environment

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A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

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Clean Energy 2022年第5期6卷 705-715页

作者： Khaled Ferkous Tayeb Boulmaiz Fahd Abdelmouiz Ziari Belgacem Bekkar Materials Energy Systems Technology and Environment LaboratoryGhardaia UniversityGhardaiaAlgeria Laboratory of Mathematical and Subatomic Physics Department of PhysicsFaculty of Exact SciencesUniversity of Constantine 1Algeria

Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and *** the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its *** this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation *** non-stationary global solar radiation series is transformed by CEEMDAN into regular *** that,the GPR model uses these subsets as inputs to perform its *** to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

关键词： hybrid models complete ensemble empirical mode decomposition with adaptive noise Gaussian process regression prediction solar measurements ghardaia site

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Optimization of the Placement and Size of Photovoltaic Source

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Computers, Materials & Continua 2023年第1期74卷 1855-1870页

作者： Maawiya Ould Sidi Mustafa Mosbah Rabie Zine RT-M2A Laboratory Mathematics DepartmentCollege of ScienceJouf UniversitySakakaSaudi Arabia university of ghardaia LACoSERE LaboratoryUniversity LaghouatAlgeria School of Science and Engineering Al Akhawayn University in IfraneMorocco

This paper presents a new optimization study of the placement and size of a photovoltaic source(PVS)in a distribution grid,based on annual records of meteorological parameters(irradiance,temperature).Based on the recorded data,the production output as well as the daily average power(24-h vector)of the PVS is extracted over the *** a power vector is available,it can be used as an input when searching for the optimal size of the *** allows to take into account the constraint of the variation of the power generated by this source considering the variation of the power consumed by the electrical loads during the whole day.A multi-objective fitness function has been *** latter minimizes the active losses and maximizes the voltage stability index during the day,while considering the constraints of the system,that is,the security,technical,geographical,and meteorological *** problem was solved using the Non-dominated Sorting Genetic Algorithm NSGA-II optimization technique under MATLAB *** was applied to the distribution network of Ghardaïa of 59 nodes.

关键词： Electric distribution network intermittent generation photovoltaic source multi-objective optimization active losses profile voltage stability index profile

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Influence of nitrogen sources on growth and mycotoxin production by isolates of Pyrenophora tritici-repentis from wheat

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The Crop Journal 2016年第2期4卷 119-128页

作者： Noureddine Bouras Michael D.Holtz Reem Aboukhaddour Stephen E.Strelkov Department of Agricultural Food and Nutritional Science410 Agriculture/Forestry CentreUniversity of Alberta Laboratoire de Biologie des Systemes Microbiens(LBSM) Ecole Normale Superieure de KoubaAlgiersAlgeria Departement de Biologie Faculte des Sciences de la Nature et de la Vie et Sciences de la TerreUniversite de Ghardaia Field Crop Development Centre Alberta Agriculture and Food

The fungus Pyrenophora tritici-repentis(Died.) Drechs. infects the leaves and kernels of wheat,causing tan spot and red smudge, respectively. Isolates of P. tritici-repentis have been reported to be both phytopathogenic and mycotoxigenic. This research investigates the influence of nitrogen sources on growth and production of mycotoxins by eight different isolates of P. tritici-repentis. A synthetic agar medium(SAM) was used with different nitrogen sources, both inorganic [(NH4Cl, NH4NO3and(NH4)2SO4)] and organic(L-alanine, L-histidine, and L-lysine), at a concentration of 37.5 mmol L-1. Individual isolates exhibited different growth rates that varied according to the nitrogen source added to the medium. The choice of nitrogen source also had a major effect on production of the mycotoxins emodin, catenarin and islandicin. The highest concentrations of emodin, 54.40 ± 4.46 μg g-1, 43.07 ± 23.39 μg g-1and28.91 ± 4.64 μg g-1of growth medium, were produced on the complex medium(V8-potato dextrose agar) by the isolates Alg-H2, 331-2 and TS93-71 B, respectively. A relatively high concentration of emodin also was produced by isolates Az35-5(28.29 ± 4.71 μg g-1of medium)and TS93-71B(27.03 ± 4.09 μg g-1of medium) on synthetic medium supplemented with L-alanine. The highest concentrations of catenarin(174.54 ± 14.46 μg g-1and 104.87 ±6.13 μg g-1of medium) were recorded for isolates TS93-71 B and Alg-H2 on synthetic medium supplemented with L-alanine and NH4 Cl, respectively. The highest concentration of islandicin(4.64 ± 0.36 μg g-1medium) was observed for isolate 331-2 in the presence of L-lysine. There was not a close relationship between mycelial growth and mycotoxin production by the fungal isolates. This is the first report on the influence of nitrogen sources on the production of mycotoxins by P. tritici-repentis.

关键词： Catenarin Emodin Islandicin Nitrogen source Tan spot Triticum aestivum

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