Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction

2020 ◽  
Vol 205 ◽  
pp. 112345 ◽  
Author(s):  
Zherui Ma ◽  
Hongwei Chen ◽  
Jiangjiang Wang ◽  
Xin Yang ◽  
Rujing Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Error Correction ◽  
Hybrid Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction ◽  
Double Decomposition

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

scholarly journals Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Aiqing Kang ◽  
Qingxiong Tan ◽  
Xiaohui Yuan ◽  
Xiaohui Lei ◽  
Yanbin Yuan
Keyword(s):  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Superposition Principle ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Mode Decomposition ◽  
Speed Prediction

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

scholarly journals Short-Term Wind Speed Prediction based on Deep Learning

2019 ◽  
Vol 233 ◽  
pp. 052007 ◽  
Author(s):  
Jingchun Chu ◽  
Ling Yuan ◽  
Wenliang Wang ◽  
Lei Pan ◽  
Jie Wei
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5595
Author(s):  
Qin Chen ◽  
Yan Chen ◽  
Xingzhi Bai
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Stationary Time Series ◽  
Percentage Error ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Interval Prediction ◽  
Stationary Time ◽  
Speed Prediction

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

A multi-location short-term wind speed prediction model based on spatiotemporal joint learning

2021 ◽  
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He ◽  
Haixin Sun
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Joint Learning ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

scholarly journals Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1822 ◽  
Author(s):  
Yuansheng Huang ◽  
Lei Yang ◽  
Shijian Liu ◽  
Guangli Wang
Keyword(s):  
Wind Speed ◽  
Error Correction ◽  
Short Term Memory ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Mode Decomposition ◽  
Forecasting Method ◽  
Speed Prediction ◽  
Long Short Term Memory

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence.

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