Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM

Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensem...

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Main Authors: Yuansheng Huang, Shijian Liu, Lei Yang
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Sustainability
Subjects:
Online Access:http://www.mdpi.com/2071-1050/10/10/3693
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spelling doaj-224a3fd142b34fe79a548e0faace7c8c2020-11-25T02:29:16ZengMDPI AGSustainability2071-10502018-10-011010369310.3390/su10103693su10103693Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTMYuansheng Huang0Shijian Liu1Lei Yang2Department of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaShort-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods.http://www.mdpi.com/2071-1050/10/10/3693short-term wind speed forecastingensemble empirical mode decompositionthe combination forecasting methodlong short-term memory neural networkGaussian process regression
collection DOAJ
language English
format Article
sources DOAJ
author Yuansheng Huang
Shijian Liu
Lei Yang
spellingShingle Yuansheng Huang
Shijian Liu
Lei Yang
Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
Sustainability
short-term wind speed forecasting
ensemble empirical mode decomposition
the combination forecasting method
long short-term memory neural network
Gaussian process regression
author_facet Yuansheng Huang
Shijian Liu
Lei Yang
author_sort Yuansheng Huang
title Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
title_short Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
title_full Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
title_fullStr Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
title_full_unstemmed Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
title_sort wind speed forecasting method using eemd and the combination forecasting method based on gpr and lstm
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2018-10-01
description Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods.
topic short-term wind speed forecasting
ensemble empirical mode decomposition
the combination forecasting method
long short-term memory neural network
Gaussian process regression
url http://www.mdpi.com/2071-1050/10/10/3693
work_keys_str_mv AT yuanshenghuang windspeedforecastingmethodusingeemdandthecombinationforecastingmethodbasedongprandlstm
AT shijianliu windspeedforecastingmethodusingeemdandthecombinationforecastingmethodbasedongprandlstm
AT leiyang windspeedforecastingmethodusingeemdandthecombinationforecastingmethodbasedongprandlstm
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