Journal
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Volume 19, Issue 5, Pages 4547-4567Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2022210
Keywords
ship power system; load forecasting; support vector machine; improved particle swarm optimization support vector machine
Categories
Funding
- Zhoushan Science and Technology Project [2022C13034]
- Jiangsu Key R D plan [BE2018007]
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This paper proposes a method for ship power load forecasting using support vector machine and improved particle swarm optimization algorithm. The experimental results show that this method can reduce prediction error, improve prediction accuracy, and is of great significance for the stability and safety of ship power system.
Compared with the land power grid, power capacity of ship power system is small,its power load has randomness. Ship power load forecasting is of great significance for the stability and safety of ship power system. Support vector machine (SVM) load forecasting algorithm is a common method of ship power load forecasting. In this paper, water flow velocity, wind speed and ship speed are used as the features of SVM to train the load forecasting algorithm, which strengthens the correlation between features and predicted values. At the same time, regularization parameter C and standardization parameter sigma of SVM has a great influence on the prediction accuracy. Therefore, the improved particle swarm optimization algorithm is used to optimize these two parameters in real time to form a new improved particle swarm optimization support vector machine algorithm (IPSO-SVM), which reduces the load forecasting error, improves the prediction accuracy of ship power load, and improves the performance of ship energy management system.
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