Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions
Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions
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新型混合 FEEMD-MLP、FEEMD-ANFIS、小波包-MLP 和小波包-ANFIS 在风速预测方面的比较
DOI：
10.1016/j.enconman.2014.09.060
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发表时间：
2015
影响因子：
10.4
通讯作者：
Li, Yan-fei
中科院分区：
文献类型：
--
作者：
Liu, Hui;Tian, Hong-qi;Li, Yan-fei
风速预测技术对于保障风电利用安全具有重要意义。与单一算法相比，混合算法在风速预测方面始终具有更好的性能。本文均采用三种最重要的分解算法[小波分解-WD/小波包分解-WPD/经验模态分解-EMD]和最新的分解算法[快速集成经验模态分解-FEEMD]，通过两种代表性网络[MLP神经网络/ANFIS神经网络]实现风速高精度预测。基于混合预测框架，提出了两种新的风速预测方法[FEEMD-MLP和FEEMD-ANFIS]。此外，还提供了一系列性能比较，包括 EMD-MLP、FEEMD-MLP、EDM-ANFIS、FEEMD-ANFIS、WD-MLP、WD-ANFIS、WPD-MLP 和 WPD-ANFIS。本研究的目的是研究不同混合模型的分解和预测性能。两个实验结果表明：（1）由于包含了分解算法，混合ANN算法比相应的单一ANN算法具有更好的性能； （2）提出的新FEEMD-MLP混合模型在三步预测中具有最佳性能，而WPD-MLP混合模型在一步预测中具有最佳性能； (3)在分解算法中，FEEMD和WPD分别比EMD和WD具有更好的性能； (4)在预测神经网络中，MLP比ANFIS具有更好的性能； (5)所有提出的混合算法都适用于风速预测。
The technology of wind speed prediction is important to guarantee the safety of wind power utilization. Compared to the single algorithms, the hybrid ones always have better performance in the wind speed predictions. In this paper, three most important decomposing algorithms [Wavelet Decomposition – WD/Wavelet Packet Decomposition – WPD/Empirical Mode Decomposition – EMD] and a latest decomposing algorithm [Fast Ensemble Empirical Mode Decomposition – FEEMD] are all adopted to realize the wind speed high-precision predictions with two representative networks [MLP Neural Network/ANFIS Neural Network]. Based on the hybrid forecasting framework, two new wind speed forecasting methods [FEEMD-MLP and FEEMD-ANFIS] are proposed. Additionally, a series of performance comparison is provided, which includes EMD-MLP, FEEMD-MLP, EDM-ANFIS, FEEMD-ANFIS, WD-MLP, WD-ANFIS, WPD-MLP and WPD-ANFIS. The aim of the study is to investigate the decomposing and forecasting performance of the different hybrid models. Two experimental results show that: (1) Due to the inclusion of the decomposing algorithms, the hybrid ANN algorithms have better performance than their corresponding single ANN algorithms; (2) the proposed new FEEMD-MLP hybrid model has the best performance in the three-step predictions while the WPD-MLP hybrid model has the best performance in the one-step predictions; (3) among the decomposing algorithms, the FEEMD and WPD have better performance than the EMD and WD, respectively; (4) in the forecasting neural networks, the MLP has better performance than the ANFIS; and (5) all of the proposed hybrid algorithms are suitable for the wind speed predictions.
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影响因子：
0.8
作者：
M. Rafei;S. E. Sorkhabi;M. Mosavi
通讯作者：
M. Rafei;S. E. Sorkhabi;M. Mosavi
影响因子：
9
作者：
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通讯作者：
A. Borchers;I. Xiarchos;Jayson Beckman
影响因子：
8
作者：
Zaccheus Olaofe Olaofe-Zaccheus-Olaofe-Olaofe-9364743
通讯作者：
Zaccheus Olaofe Olaofe-Zaccheus-Olaofe-Olaofe-9364743
影响因子：
8.5
作者：
Goyal, Manish Kumar;Bharti, Birendra;Pandey, Ashish
通讯作者：
Pandey, Ashish
使用 GA 和 PSO 优化进行风速预测的两种小波-MLP 混合框架的实验研究
DOI：
10.1016/j.ijepes.2013.03.034
发表时间：
2013-11
期刊：
Electrical Power and Energy Systems
影响因子：
--
作者：
Hui Liu;Hong-qi Tian;Chao Chen;Yan-fei Li
通讯作者：
Yan-fei Li