S. Mishra, R. K. Patnaik, P. K. Dash, R. Bisoi, J. Naik
{"title":"An EMD Based Polynomial Kernel Methodology for superior Wind Power Prediction.","authors":"S. Mishra, R. K. Patnaik, P. K. Dash, R. Bisoi, J. Naik","doi":"10.1109/AiDAS47888.2019.8970690","DOIUrl":null,"url":null,"abstract":"This paper proposes low complexity Empirical mode decomposition trained by Kernel based (KEMD) algorithm for wind power prediction for various time horizon such as ten minutes to five hours interval for California wind farm. For a comparative performance analysis, another two forecasting model named as Empirical mode decomposition with pseudo inverse neural network and Pseudo Inverse neural network with Legendre functions and RBF units, which is further optimized by Firefly algorithm (FFA) is described here. Kernel based pseudo inverse algorithm is proposed because it eliminates the involvement of the hidden layers in each iteration, which helps in return to reduce the computational complexity and generates more precise response in prediction purpose. In the other two models the weights which are used between the hidden layer and the output neuron are obtained by PINN which is also known as Moore-Penrose pseudo inverse algorithm. This proposed KEMD trained by kernel based pseudo inverse algorithm has a very good and precise prediction of wind power. This model has been proved by doing several observations for various seasons which has been demonstrated in the results and simulation section.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AiDAS47888.2019.8970690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes low complexity Empirical mode decomposition trained by Kernel based (KEMD) algorithm for wind power prediction for various time horizon such as ten minutes to five hours interval for California wind farm. For a comparative performance analysis, another two forecasting model named as Empirical mode decomposition with pseudo inverse neural network and Pseudo Inverse neural network with Legendre functions and RBF units, which is further optimized by Firefly algorithm (FFA) is described here. Kernel based pseudo inverse algorithm is proposed because it eliminates the involvement of the hidden layers in each iteration, which helps in return to reduce the computational complexity and generates more precise response in prediction purpose. In the other two models the weights which are used between the hidden layer and the output neuron are obtained by PINN which is also known as Moore-Penrose pseudo inverse algorithm. This proposed KEMD trained by kernel based pseudo inverse algorithm has a very good and precise prediction of wind power. This model has been proved by doing several observations for various seasons which has been demonstrated in the results and simulation section.
本文提出了一种基于Kernel based (KEMD)算法训练的低复杂度经验模态分解方法,用于加州风电场10分钟至5小时间隔等不同时间范围内的风电预测。为了进行性能对比分析,本文描述了另外两种预测模型,分别是基于伪逆神经网络的经验模态分解模型和基于Legendre函数和RBF单元的伪逆神经网络模型,并通过Firefly算法(FFA)进行了进一步优化。提出了基于核的伪逆算法,因为它在每次迭代中消除了隐藏层的介入,从而有助于降低计算复杂度,在预测目的上产生更精确的响应。在另外两种模型中,隐含层与输出神经元之间的权值由PINN(也称为Moore-Penrose伪逆算法)获得。本文提出的基于核的伪逆算法训练的KEMD具有很好的风电预测精度。该模式已通过对不同季节的多次观测得到证实,结果和模拟部分已对此进行了论证。