Enhanced Multi-Step Ahead Forecasting of Electrical Power Demand Using Neighborhood-Constrained Decomposition Strategy

This paper presents a novel hybrid forecasting approach that integrates time series decomposition with machine learning to enhance prediction accuracy. The proposed method employs Long Short-Term Memory (LSTM) networks to generate predictions from multivariate input time series, which are first decomposed using a newly developed algorithm derived from Variational Mode Decomposition (VMD), the Neighborhood Constrained Variational Mode Decomposition (NCVMD). NCVMD decomposes multivariate input time series into band-limited modes, prioritizing a main signal, the target forecast, over auxiliary signals and aligning their central frequencies within a controllable range. This alignment enables cooperative forecasting, wherein predictions from individual modes are integrated to form the final output. The proposed method is compared to a similar hybrid model based on traditional VMD. Experimental results obtained using multivariate input time series, where each variable represents a different component of a large passenger ship's power demand, demonstrate that the proposed NCVMD-based approach delivers superior forecasting performance, highlighting its effectiveness for complex signal prediction tasks.