Research on load combination forecasting of ship integrated power system based on neural network
With the rapid development of pure electric ships,the impact of their electricity load on electricity market transactions is becoming increasingly prominent.Therefore,this paper proposes a ship integrated power system load neural network combination prediction method,aiming to improve prediction accuracy.Firstly,analyze the load characteristics of the integrated power system of pure electric ships under various operating conditions.Then,study the load forecasting meth-od for ship integrated power system based on typical neural networks,and reveal its limitations in predicting complex work-ing conditions.In response to the above issues,a load combination prediction method for ship integrated power system based on a combination of BP and RBF neural networks is proposed.This combined prediction method combines the advantages of BP and RBF neural network models,improving the generalization ability and fault tolerance of the prediction model.Finally,taking a pure electric ship in Jiangsu as an actual calculation example,a comparative prediction of the comprehensive power system load of the ship under complex working conditions is conducted.The results show that compared with a single pre-diction algorithm,the proposed method improves the prediction accuracy from 96.63％to 98.98％.
ship power systemload forecastingBP neural networkRBF neural network
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