Work

JOVET: Jointly optimized multi-variational mode embedded Transformer framework for wind-to-hydrogen LCOH forecasting

Year: 2026

Type: article

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.

Source: International Journal of Hydrogen Energy

DOI: 10.1016/j.ijhydene.2026.156668

Authors: Doha Bounaim, Sara Mouafik, Gang Li

Institution: Mississippi State University

Language: English

Topic: Hybrid Renewable Energy Systems

Subfield: Energy Engineering and Power Technology

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,730