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Neural Network-Based Ship Power Load Forecasting
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DOI：10.3390/jmse13091766.png)
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Abstract
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This study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network effectively forecasts the power load. Subsequently, addressing the limitations of traditional BP neural networks, an optimization approach employing an enhanced particle swarm algorithm and attention mechanism was developed, thereby improving the model’s prediction accuracy and robustness. The experiment shows that the improved prediction model achieves an R2 value of 97.42%, demonstrating its effectiveness in forecasting changes in the short-term power load of ships as parameters change. In actual operation, ships can allocate power reasonably and in a timely manner according to the load forecast results, thereby improving the efficiency of the power grid.
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