A Hybrid Transformer-Generative Adversarial Network-Gated Recurrent Unit Model for Intelligent Load Balancing and Demand Forecasting in Smart Power Grids

Ata Larijani; Ehsan Ghafourian; Ali Vaziri; Diego Martín; F. Hernando-Galego

doi:10.3390/electronics15081579

Publication record · 2026

A Hybrid Transformer-Generative Adversarial Network-Gated Recurrent Unit Model for Intelligent Load Balancing and Demand Forecasting in Smart Power Grids

Authors: Ata Larijani · Ehsan Ghafourian · Ali Vaziri · Diego Martín · F. Hernando-Galego
Year: 2026
Publisher: MDPI
Journal or publication: Electronics
Google Scholar citations: 0

Journal indicators

CiteScore 2025 (Scopus)1.3

Best quartileQ3

Top 10% in any categoryNo

Positions by category

Electrical and Electronic Engineering732/1030 · Q3 · percentile 28

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JCR (Clarivate): Not verified: the supplied ranking file is Scopus CiteScore, not JCR.

Included in the supplied report on non-standard bibliometric behaviour (2017–2019).

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Research summary

Transformer-GAN-GRU integrates global temporal attention, generative augmentation and recurrent refinement for demand forecasting and load balancing. Evaluation on Pecan Street, RTS-GMLC and REDD tests predictive performance, stability and inference latency.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.3390/electronics15081579 ↗ · Open-access version ↗ · View Google Scholar record ↗