NCD-Pred: Forecasting Multichannel Shipboard Electrical Power Demand Using Neighborhood-Constrained VMD

Paolo Fazzini, Giuseppe La Tona (), Marco Montuori, Matteo Diez and Maria Carmela Di Piazza
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Paolo Fazzini: Institute of Marine Engineering (INM), National Research Council (CNR), 90153 Palermo, Italy
Giuseppe La Tona: Institute of Marine Engineering (INM), National Research Council (CNR), 90153 Palermo, Italy
Marco Montuori: Institute of Complex Systems (ISC), National Research Council (CNR), 00185 Rome, Italy
Matteo Diez: Institute of Marine Engineering (INM), National Research Council (CNR), 00128 Rome, Italy
Maria Carmela Di Piazza: Institute of Marine Engineering (INM), National Research Council (CNR), 90153 Palermo, Italy

Forecasting, 2025, vol. 7, issue 3, 1-19

Abstract: This paper introduces Neighborhood-Constrained Decomposition-based Prediction (NCD-Pred), the first system to leverage Neighborhood-Constrained Variational Mode Decomposition (NCVMD) for multichannel forecasting by integrating time series decomposition and neural networks. NCD-Pred leverages NCVMD to decompose a multichannel signal into simpler, band-limited components—referred to as intrinsic mode functions or simply modes —by prioritizing the most informative channel (the main channel) over less informative ones (the auxiliary channels) and bringing their central frequencies into alignment up to a tunable extent. This frequency synchronization provides a framework for cooperative mode forecasting, where predictions of signal components are recombined to produce the original signal prediction. For mode-level forecasting, Long Short-Term Memory (LSTM) networks are utilized. NCD-Pred’s performance is evaluated against similarly designed mode-level forecasting systems using a multichannel dataset with weak cross-correlation, representing power load on a large vessel. The results show that NCD-Pred outperforms benchmark methods, demonstrating its practical utility in real signal processing scenarios.

Keywords: energy management; shipboard electrical power consumption; forecasting; machine learning; multi-channel (multivariate) forecasting; Variational Mode Decomposition (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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