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IoT-driven anomaly detection in smart grids using multimodal deep learning models

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Bibliographic Details
Published in:Turkish Journal of Energy Policy
Format: Online Article RSS Article
Published: 2026
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FRELIP Feed Integration
container_title Turkish Journal of Energy Policy
description
discipline_display Environmental Sciences
discipline_facet Environmental Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:30815
institution FRELIP
journal_source_facet Turkish Journal of Energy Policy
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle IoT-driven anomaly detection in smart grids using multimodal deep learning models
— — — — — Energy
Environmental Management
Environmental Sciences
sub_discipline_display Environmental Management
sub_discipline_facet Environmental Management
subject_display — — — — — Energy
Environmental Management
Environmental Sciences
— — — — — Energy
Environmental Management
Environmental Sciences
subject_facet — — — — — Energy
Environmental Management
Environmental Sciences
title IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_auth IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_full IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_fullStr IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_full_unstemmed IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_short IoT-driven anomaly detection in smart grids using multimodal deep learning models
title_sort iot-driven anomaly detection in smart grids using multimodal deep learning models
topic — — — — — Energy
Environmental Management
Environmental Sciences
url https://dergipark.org.tr/en/pub/ijes/article/1815151%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20