Application of Least Squares Support Vector Machine Based on Particle Swarm Optimization in Tidal Current Prediction of Offshore Microgrid

Haiyun Yuan, Song Xu, Yangfan Sun, Qian Li, Anan Zhang
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Abstract

In order to reduce the impact of the uncertainty of tidal current and improve the application of tidal current energy in offshore microgrid, the tidal current predict is of importance. Through studying the characteristics of the tidal current time series of the offshore microgrid, a tidal current prediction model based on the least squares support vector machine is proposed, and the key parameters of the model are optimized by the particle swarm optimization algorithm. At last, case studies based on the actual data of an offshore microgrid are carried out to verify the accuracy of the proposed model.
基于粒子群优化的最小二乘支持向量机在近海微电网潮流预测中的应用
为了减少潮流不确定性的影响,提高潮流能在近海微电网中的应用,潮流预测具有重要意义。通过研究近海微电网潮流时间序列的特点,提出了一种基于最小二乘支持向量机的潮流预测模型,并采用粒子群优化算法对模型的关键参数进行了优化。最后,基于海上微电网的实际数据进行了实例研究,验证了所提模型的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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