Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system

IF 3.8 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-17 DOI:10.1016/j.suscom.2025.101083

Mohand Akli Kacimi , Celia Aoughlis , Toufik Bakir , Ouahib Guenounou

{"title":"Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system","authors":"Mohand Akli Kacimi , Celia Aoughlis , Toufik Bakir , Ouahib Guenounou","doi":"10.1016/j.suscom.2025.101083","DOIUrl":null,"url":null,"abstract":"<div><div>This paper deals with a topical topic, the maximum energy harvest of standalone PV system under varying conditions. It introduces a new approach based on the use of artificial intelligence and machine learning to overcome the usual weaknesses of conventional Maximum Power Point Tracking (MPPT) techniques and to improve solutions to meet growing energy demand and further promote sustainable development. The proposal consists of using Radial Basis Function Neural Network (RBFNN) tuned by a PSO algorithm as MPPT controller. The main aim of this combination (RBFNN-PSO) is to achieve the best compromise between the control accuracy and complexity, while using a simple optimization algorithm. This aim is motivated by the potential of the neural networks to learn from any tasks and to generalize the acquired knowledge to other situation never seen before. The proposal reaches a high efficiency and high energy harvesting with a yield greater than 99 %. The performed comparative study with other intelligent techniques from literature prove the superiority and the promising potential of the introduced approach. The developed work presented in this paper is developed with MatLab/Simulink environment.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101083"},"PeriodicalIF":3.8000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Computing-Informatics & Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210537925000034","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}

引用次数: 0

Abstract

This paper deals with a topical topic, the maximum energy harvest of standalone PV system under varying conditions. It introduces a new approach based on the use of artificial intelligence and machine learning to overcome the usual weaknesses of conventional Maximum Power Point Tracking (MPPT) techniques and to improve solutions to meet growing energy demand and further promote sustainable development. The proposal consists of using Radial Basis Function Neural Network (RBFNN) tuned by a PSO algorithm as MPPT controller. The main aim of this combination (RBFNN-PSO) is to achieve the best compromise between the control accuracy and complexity, while using a simple optimization algorithm. This aim is motivated by the potential of the neural networks to learn from any tasks and to generalize the acquired knowledge to other situation never seen before. The proposal reaches a high efficiency and high energy harvesting with a yield greater than 99 %. The performed comparative study with other intelligent techniques from literature prove the superiority and the promising potential of the introduced approach. The developed work presented in this paper is developed with MatLab/Simulink environment.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Sustainable Computing-Informatics & Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTUREC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

10.70

自引率

4.40%

发文量

142

期刊介绍： Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.