短期风电功率预测中的IOFA-SVM算法实现  

Improved optimal foraging algorithm for support vector machine of short-term wind power prediction

作　　者：谢波 高建宇 张惠娟[1] 刘金委 Xie Bo;Gao Jianyu;Zhang Huijuan;Liu Jinwei(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;Qinggong College,North China University of Science and Technology,Tangshan 063000,China)

机构地区：[1]河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津300130 [2]华北理工大学轻工学院,唐山063000

出　　处：《电子测量技术》2021年第12期63-69,共7页Electronic Measurement Technology

基　　金：天津市自然科学基金重点项目(19JCZDJC32100)资助。

摘　　要：在风电等清洁能源的开发和应用中,为提高风电输出功率预测精度,设计出改进最优觅食算法-优化支持向量机(IOFA-SVM)预测模型,在传统最优觅食算法中加入柯西变异和差分进化策略来提高算法的全局寻优能力以获取SVM的最优参数。用改进后的IOFA-SVM模型进行预测,并将预测结果与BP、GWO-SVM、OFA-SVM模型进行对比,在相同的条件和参数下,该模型3种评价指标MAE、NMAE和NRMSE至少下降0.59%、0.53%和0.50%,表明IOFA-SVM模型确实提高了风电功率预测精度和准确性,对电能调度和电网稳定运行具有重要意义。In development and application of wind energy, for improving the prediction accuracy of wind power output, a prediction model based on improved optimal foraging algorithm for support vector machine(IOFA-SVM)is proposed. Cauchy variation and differential mutation strategy are added into the traditional optimal foraging algorithm to improve the global optimization ability to obtain the optimal parameters of SVM. Using the improved IOFA-SVM model to predict wind power output and comparing the results with BP, GWO-SVM and OFA-SVM models, the three evaluation indexes MAE, NMAE and NRMSE of the model decreased by 0.59%, 0.53% and 0.50% respectively, which shows that the IOFA-SVM model does improve the precision and accuracy of wind power output prediction, and is important to dispatch electric energy and maintain power grid stability.

关 键 词：风电功率预测 最优觅食算法 支持向量机 柯西变异优化 差分进化策略 

分 类 号：TM614[电气工程—电力系统及自动化]