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Very-Short Term Wind Power Forecasting through Wavelet Based ANFIS

机译:基于小波的ANFIS的短期风电预测

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This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
机译:本文提出了一种基于小波的自适应神经模糊推理系统(WANFIS),该技术可用于预测风能并通过从印度北部风电场采集的实时数据达到10分钟的分辨率来提高向前一步的准确性。所提出的方法包括两种情况。在第一种情况下,所有风系列的输入和风能分解系数的输出都被用来预测风能。在第二种情况下,进行风序列分解系数的所有输入以获得风能预测。将所提出的WANFIS的性能与小波神经网络(WNN)进行了比较,并显示出所模型的结果优于比较方法。

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