Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm

•A new hybrid method is proposed for the wind speed multi-step forecasting.•The wavelet decomposition is adopted to reduce the noise of the original data.•The different decomposing algorithms are used to decompose the original data.•The different forecasting models are built to predict the pre-proce...

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Vydáno v:	Energy conversion and management Ročník 156; s. 525 - 541
Hlavní autoři:	Liu, Hui, Duan, Zhu, Han, Feng-ze, Li, Yan-fei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 15.01.2018 Elsevier Science Ltd
Témata:	Algorithms Back propagation networks Big multi-step wind speed forecasting Computer simulation Decomposition Electricity distribution Entropy Error correction Forecasting Machine learning Mathematical models Modified adaBoost.RT neurons Sample entropy Studies Variation Variational mode decomposition wavelet Wavelet analysis Wavelet decomposition Wavelet filter Wind power Wind speed ANN CRO Wavelet filter HM ELM FEEMD ARCH Variational mode decomposition WRF WPD SVM FS SPSA WD WF Wavelet decomposition MLP PSOGSA Big multi-step wind speed forecasting EEMD Sample entropy KF EMD ADMM MAdaBoost SSA DWT CWT LMD DNN-MRT NWP Modified adaBoost.RT MAPE RMSE VMD BFGS CS MAE AR FCM AdaBoost ARIMA LSSVM SampEn MFNN
ISSN:	0196-8904, 1879-2227
On-line přístup:	Získat plný text
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Shrnutí:	•A new hybrid method is proposed for the wind speed multi-step forecasting.•The wavelet decomposition is adopted to reduce the noise of the original data.•The different decomposing algorithms are used to decompose the original data.•The different forecasting models are built to predict the pre-processed data.•The error correction method is proposed to correct the wrong predictions. Wind power is one of the most promising powers. Wind speed forecasting can eliminate the harmful effect caused by the intermittent and fluctuation of wind power, and big multi-step forecasting can provide more time for the power grid to be adjusted. To achieve the high-precision big multi-step forecasting, a novel hybrid model named as the WD-SampEn-VMD-MadaBoost-BFGS-WF is proposed in the study, which consisting of three main modeling steps including the secondary decomposition, the ensemble method and the error correction. The detail of the proposed model is given as follows: (a) wind speed series are decomposed by the WD (Wavelet Decomposition) to obtain wind speed subseries. The SampEn (Sample Entropy) algorithm is used to estimate the unpredictability of these wind speed subseries. The most unpredictable subseries will be decomposed secondarily by the VMD (Variational Mode Decomposition); (b) the subseries are proceeded by the MAdaBoost (Modified AdaBoost.RT) with the BFGS (Broyden–Fletcher–Goldfarb–Shanno Quasi-Newton Back Propagation) neuron network to obtain forecasting subseries; (c) all of the forecasting subseries will be combined with the original subseries to form the combined wind speed series, which will be further proceeded by the WF (Wavelet Filter) to obtain the corrected forecasting series from the point of the frequency domain; (d) the corrected forecasting series are reconstructed to get the final forecasting series. To validate the effectiveness of the proposed model, several forecasting cases are provided in the study. The result indicates that the proposed model has satisfactory forecasting performance in the big multi-step extremely strong simulating wind speed forecasting.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0196-8904 1879-2227
DOI:	10.1016/j.enconman.2017.11.049