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Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions

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Published in:IEEE Access
Format: Online Article RSS Article
Published: 2026
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container_title IEEE Access
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:9494
institution FRELIP
journal_source_facet IEEE Access
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_auth Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_full Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_fullStr Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_full_unstemmed Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_short Multi-Objective Optimal Design of an On-Grid Hybrid PV–Wind–Battery System Using Neural Network-Based Predictions
title_sort multi-objective optimal design of an on-grid hybrid pv–wind–battery system using neural network-based predictions
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://ieeexplore.ieee.org/document/11408778