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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06153-z |
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| author | Paolo Fazzini Giuseppe La Tona Matteo Diez Maria Carmela Di Piazza |
| author_facet | Paolo Fazzini Giuseppe La Tona Matteo Diez Maria Carmela Di Piazza |
| author_sort | Paolo Fazzini |
| collection | DOAJ |
| description | 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 combines multivariate time series decomposition with Machine Learning (ML) techniques. Specifically, the method utilizes Long Short-Term Memory (LSTM) networks to generate forecasts from multivariate input time series that have been decomposed using a newly formulated Variational Mode Decomposition (VMD), termed Variational Mode Decomposition with Mode Selection (VMDMS). VMDMS enables a selective detection process, identifying modes across channels that synergistically enhance forecasting accuracy. The proposed hybrid forecasting method is validated using a dataset of electric power demand time series collected from a real-world large passenger ship. Experimental results confirm the effectiveness of the approach, extending the applicability of VMD to multivariate forecasting without imposing restrictive assumptions on the data. This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency. |
| format | Article |
| id | doaj-art-7ebadcd9ba504ca4ad5a6d599d3db571 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7ebadcd9ba504ca4ad5a6d599d3db5712025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-06153-zEnhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selectionPaolo Fazzini0Giuseppe La Tona1Matteo Diez2Maria Carmela Di Piazza3Institute of Marine Engineering (INM), National Research Council (CNR)Institute of Marine Engineering (INM), National Research Council (CNR)Institute of Marine Engineering (INM), National Research Council (CNR)Institute of Marine Engineering (INM), National Research Council (CNR)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 combines multivariate time series decomposition with Machine Learning (ML) techniques. Specifically, the method utilizes Long Short-Term Memory (LSTM) networks to generate forecasts from multivariate input time series that have been decomposed using a newly formulated Variational Mode Decomposition (VMD), termed Variational Mode Decomposition with Mode Selection (VMDMS). VMDMS enables a selective detection process, identifying modes across channels that synergistically enhance forecasting accuracy. The proposed hybrid forecasting method is validated using a dataset of electric power demand time series collected from a real-world large passenger ship. Experimental results confirm the effectiveness of the approach, extending the applicability of VMD to multivariate forecasting without imposing restrictive assumptions on the data. This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.https://doi.org/10.1038/s41598-025-06153-zEnergy ManagementShipboard electrical power consumptionForecastingMachine LearningMultivariate ForecastingVariational Mode Decomposition |
| spellingShingle | Paolo Fazzini Giuseppe La Tona Matteo Diez Maria Carmela Di Piazza Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection Scientific Reports Energy Management Shipboard electrical power consumption Forecasting Machine Learning Multivariate Forecasting Variational Mode Decomposition |
| title | Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| title_full | Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| title_fullStr | Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| title_full_unstemmed | Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| title_short | Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| title_sort | enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection |
| topic | Energy Management Shipboard electrical power consumption Forecasting Machine Learning Multivariate Forecasting Variational Mode Decomposition |
| url | https://doi.org/10.1038/s41598-025-06153-z |
| work_keys_str_mv | AT paolofazzini enhancedforecastingofshipboardelectricalpowerdemandusingmultivariateinputandvariationalmodedecompositionwithmodeselection AT giuseppelatona enhancedforecastingofshipboardelectricalpowerdemandusingmultivariateinputandvariationalmodedecompositionwithmodeselection AT matteodiez enhancedforecastingofshipboardelectricalpowerdemandusingmultivariateinputandvariationalmodedecompositionwithmodeselection AT mariacarmeladipiazza enhancedforecastingofshipboardelectricalpowerdemandusingmultivariateinputandvariationalmodedecompositionwithmodeselection |