Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection

Abstract Accurate forecasting of shipboard electricity demand is essential for optimizing Energy Management Systems (EMSs), which are crucial for efficient and profitable operation of shipboard power grids. To address this challenge, this paper introduces a novel hybrid forecasting approach that com...

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Bibliographic Details
Main Authors: Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Energy Management
Shipboard electrical power consumption
Forecasting
Machine Learning
Multivariate Forecasting
Variational Mode Decomposition
Online Access:https://doi.org/10.1038/s41598-025-06153-z
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https://doi.org/10.1038/s41598-025-06153-z

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