基于ikPCA-FABAS-KELM的短期风电功率预测

Short-term wind power prediction based on ikPCA-FABAS-KELM

为了增强在短期风电功率预测领域中传统数据驱动机器学习模型的精度,提出基于ikPCA-FABAS-KELM的短期风电功率预测模型.首先,对主成分分析进行改进,提出可逆核主成分分析(ikPCA),在保证数据特征的同时,降低输入数据的复杂度,以提升模型运行速度;其次,引入萤火虫个体吸引策略对天牛须算法(BAS)进行改进,提出FABAS算法;最后,利用FABAS算法对核极限学习机(KELM)的正则化参数C和核参数γ进行寻优,降低人为因素对模型盲目训练的影响,提高模型预测精度.仿真结果显示,提出的预测模型有效提高了传统模型的预测精度.

A prediction model based on ikPCA-FABAS-KELM is proposed to improve the short-term wind power prediction by traditional data-driven machine learning models.First,the principal component analysis is improved and the reversible kernel Principal Component Analysis(ikPCA)is proposed to reduce the complexity of input data while ensuring data features,with the purpose to advance the model in running speed.Second,the individual attrac-tion strategies for Firefly Algorithm(FA)are used to improve the Beetle Antennae Search(BAS)thus a FABAS algorithm is proposed.Finally,the FABAS algorithm is used to optimize the regularization parameter C and kernel parametersγof the Kernel Extreme Learning Machine(KELM),which can reduce the impact of manual parameter setting on blind model training thus improve model prediction accuracy.The simulation results show that the proposed model effectively improves the short-term wind power prediction accuracy.