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

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).

Effects of deployment of electric vehicles on air quality in the urban area of Turin (Italy).

This study aims to evaluate and quantify the environmental, health, and economic benefits due to the penetration of electric vehicles in the fleet composition by replacing conventional vehicles in an urban area. This study has been performed for the city of Turin, where road transport represents one of the main primary emission sources. Air pollution data were evaluated by ADMS-Roads, the flow traffic data used for simulation come from a real-time monitoring.