JOVET: Jointly Optimized Multi-Variational Mode Embedded Transformer Framework for Wind-to-Hydrogen LCOH Forecasting

ORCID

Bounaim: https://orcid.org/0009-0008-5853-5223; Mouafik: https://orcid.org/0009-0003-5483-7455; Li: https://orcid.org/0000-0003-2793-4615

MSU Affiliation

James Worth Bagley College of Engineering; Michael W. Hall School of Mechanical Engineering

Creation Date

2026-09-30

Abstract

This paper proposes a jointly optimized multi-variational mode embedded Transformer (JOVET) framework that improves levelized cost of hydrogen (LCOH) forecasting reliability for wind-to-hydrogen systems under uncertainty. In contrast to disjointed hybrid pipelines, JOVET is a unified embedded framework incorporating joint particle swarm optimization, jointly tuning multi-variational mode decomposition and Transformer hyperparameters. Embedding multi-variational modes into a multi-head-attention Transformer filters stochastic noise from supervisory control and data acquisition data, producing more stable LCOH forecasts. Benchmarked against time-series and decomposition-based models, JOVET achieves a mean absolute error of 77.34 kW, root mean square error of 124.38 kW, mean absolute percentage error of 7.36%, and coefficient of determination of 0.9914 for wind power forecasting, outperforming the best non-embedded baselines. Propagated to annual levelized cost metrics for alkaline and proton exchange membrane electrolyzers, JOVET yields an annual relative error of 0.007%, the smallest deviation among baselines, which propagates into the tightest LCOH distribution.

Keywords

multi-variational mode, embedded transformer, time-series forecasting, wind-to-hydrogen system, LCOH

Publication Date

7-24-2026

Publication Title

International Journal of Hydrogen Energy

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2026 The Authors

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Digital Object Identifier (DOI)

https://doi.org/10.1016/j.ijhydene.2026.156668