# A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

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## Abstract

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## 1. Introduction

## 2. Motivations behind Wind Data Prediction

## 3. Conventional Models for Wind Data Prediction

## 4. Empirical Mode Decomposition

- (1)
- the mean of lower and upper envelopes tends to zero, and
- (2)
- the number of extrema and zero crossing differs at most by one.

## 5. Improvements in EMD

## 6. Motivations for Proceeding with EMD

- Apart from unique signal decomposition, IMFs (generated with EMD) have good local characteristics in both time as well as frequency domains [27].
- The working principle of EMD is empirical without any mathematical/statistical calculations and hence is very easy to understand [28].
- EMD is empirical, intuitive, direct and analyzes multi-component signals with predetermined basis functions [13].
- EMD can handle complex valued time series very efficiently [54].
- EMD decreases the instability of wind data and hence minimizes the difficulties in high precision predictions [48].
- After addition of all IMFs, the coupling of characteristics information gets reduced and hence original signal gets reconstructed more accurately [26].

## 7. Intrinsic Mode Functions

## 8. Review on EMD/EEMD Based Ensemble Methods for Wind Data Prediction

#### 8.1. Artificial Neural Networks

#### 8.1.1. EMD-BPNN Models

#### 8.1.2. EEMD-BPNN Model

#### 8.1.3. EMD-GABP Model

#### 8.1.4. EEMD-GABP Model

#### 8.1.5. EMD-ENN Model

#### 8.1.6. EEMD-ENN Model

#### 8.1.7. EMD-RBFNN Model

#### 8.1.8. EEMD-FNN Model

#### 8.1.9. EEMD-WNN Model

#### 8.1.10. EMD-LMNN Model

#### 8.1.11. EEMD-MLP Models

#### 8.1.12. EEMD-ELM Models

#### 8.2. Support Vector Machines and Least Square SVM

#### 8.2.1. EMD-SVM Models

#### 8.2.2. EEMD-SVM Models

#### 8.2.3. EMD-LSSVM Models

#### 8.2.4. EEMD-LSSVM Models

#### 8.2.5. EMD-RVM Models

#### 8.2.6. EEMD-RVM Models

#### 8.3. Statistical Models

#### 8.3.1. EMD-Autoregression Models

#### 8.3.2. EEMD-Autoregression Model

- The hybridization of EMD/EEMD with an autoregression model improved the prediction accuracy as compared to simple autoregression models for wind data sets.
- In most of the models (reviewed in this section), the autoregression methods were combined with other methods such as ANN and LSSVM and were found to be more suitable for low-frequency components, while the other methods were kept restricted for IMFs with higher frequency components.

#### 8.3.3. EMD-kNN Model

#### 8.3.4. EEMD-PSF Model

#### 8.4. Chaotic Theory Treatments

## 9. Measures to Estimate Prediction Errors

## 10. Discussion

## 11. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ANFIS | Adaptive Neural Network based Fuzzy Interface System |

ANN | Artificial Neural Networks |

AR | Autoregression |

ARIMA | Autoregressive Integrated Moving Average |

ARMA | Autoregressive Moving Average |

BA | Bat Algorithm |

BPNN | Back-Propagation Neural Networks |

CEEMD | Complete Ensemble Empirical Mode Decomposition |

CEEMDAN | Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |

CS | Cuckoo Search |

DSF | Decomposition Selection Forecasting |

ELM | Extreme Learning Machine |

EEMD | Ensemble Empirical Mode Decomposition |

EMD | Empirical Mode Decomposition |

ENN | Elman Neural Networks |

ESN | Echo State Network |

EWT | Empirical Wavelet Transform |

FEEMD | Fast Ensemble Empirical Mode Decomposition |

FNN | Feed-forward Neural Network |

GABP | Genetic Algorithm Back-Propagation Neural Network |

HS | Harmony Search |

IMF | Intrinsic Mode Function |

kNN | k - Nearest Neighbors |

LFO | Local First Order |

LMNN | Levenberg-Marquardt Neural Network |

LSSVM | Least Squares Support Vector Machine |

MA | Moving Average |

MAE | Mean Absolute Error |

MAPE | Mean Absolute Percentage Error |

MEA | Mind Evolutionary Algorithm |

MkRVR | Multiple-kernel Relevance Vector Regression |

MLP | Multilayer Perceptron |

MMLP | Mathematical Morphologybased Local Predictor |

MTD | Mean Trend Detector |

NWP | Numerical Weather Prediction |

PACF | Partial Auto-correlation Function |

PCA | Principle Component Analysis |

PolyRVR | Polynomial kernel Multiple-kernel Relevance Vector Regression |

PSF | Pattern Sequence based Forecasting |

PSO | Particle Swarm Optimization |

RARIMA | Recursive Autoregression of Integrated Moving Average |

RBFNN | Radial Basis Function Network |

RELM | Regularized Extreme Learning Machine |

RMSE | Root Mean Square Error |

RT | Runs Test |

RVM | Relevance Vector Machine |

SARIMA | Seasonal Autoregressive Integrated Moving Average |

SD | Standard Deviation |

SSA | Singular Spectrum Analysis |

SVD | Singular Value Decomposition |

SVM | Least Squares Support Vector Machine |

SWEMD | Sliding Window Empirical Mode Decomposition |

VMD | Variation Mode Decomposition |

WD | Wavelet Decomposition |

WNN | Weighted Neural Network |

WPD | Wavelet Packet Decomposition |

WT | Wavelet Transform |

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**Figure 6.**A sample wind speed data and finite number of Intrinsic Mode Functions (IMFs) generated with the EMD method.

Prediction Models | Artificial Intelligence Methods | Statistical Methods | Chaotic Methods | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

ANN | SVM/LSSVM | AR/ARMA/ARIMA | - | ||||||||

Methods | EMD | EEMD | Methods | EMD | EEMD | Methods | EMD | EEMD | EMD | EEMD | |

BPNN | Hong et al. [7] Liu et al. [8] Ren et al. [9] Guo et al. [10] | Ren et al. [11] Zhang et al. [12] | SVM | Ren et al. [13] Zhang et al. [14] Wang et al. [15] Zhang et al. [16] Han and Zhu [17] Lin and Peng [18] | Hu et al. [19] Ren et al. [11] Jia [20] | AR/ARMA/ARIMA | Tatinati and Veluvolu [21] Hong et al. [22] Liu et al. [23] Xingjie et al. [24] Liu et al. [25] Li and Wang [26] Sun et al. [27] | Zhang et al. [28] | Drisya and Kumar [29] | An et al. [30] Zhang Xue-Qing [31] | |

GABP | Wang et al. [32] | Wang et al. [33] Wang et al. [34] | |||||||||

ENN | Wang et al. [35] Liu et al. [36] | Yu et al. [37] Xingjie et al. [24] Zhang et al. [12] | |||||||||

RBFNN | Zheng et al. [38] | Zhang et al. [12] | LSSVM | Sun and Yuan [39] Tatinati and Veluvolu [21] Liu et al. [23] Xingjie et al. [24] Liu et al. [25] Li and Wang [26] | Jiang and Huang [40] Wu and Peng [41] Safari et al. [42] Sun et al. [43] | ||||||

FNN | - | Wang et al. [44] | PSF | Bokde et al. [45] | |||||||

WNN | - | Zhang et al. [46] Zhang et al. [12] | |||||||||

LMNN | Dokur et al. [47] Zhang et al. [14] | - | |||||||||

MLP | - | Liu et al. [48] Liu et al. [49] | RVM | Bao et al. [50] Fei [51] | Zang et al. [52] Zang et al. [53] | k-NN | Ren and Suganthan [54] | - | |||

ELM | - | Liu et al. [55] Sun and Liu [56] |

Motivations for Wind Data Prediction | Articles | ||
---|---|---|---|

1 | Selection of land/place for wind farm establishment | Hu et al. [19] | |

2 | Power grid operations and energy generation | ||

a. | Dispatch planning | Ren et al. [13], Sun and Yuan [39], Liu et al. [8], Dokur et al. [47], Liang et al. [58], Wu and Peng [41], Bao et al. [50], Wang et al. [44], Zhang et al. [46], Zhang et al. [12] | |

b. | Unit commitment decisions | Ren et al. [13], Hong et al. [7], Hu et al. [19], Guo et al. [10], Wu and Peng [41], Ren et al. [11] | |

c. | Wind farm regulations | Ren et al. [13], Guo et al. [10], Zhang et al. [46] | |

d. | Maintenance and program scheduling | Dokur et al. [47], Xiaolan and Hui [59], Ren et al. [13], Guo et al. [10], Hu et al. [19] Liang et al. [58], Zang et al. [52], Xingjie et al. [24], Liu et al. [55], Bao et al. [50] Zhang et al. [46], An et al. [30], Dejun et al. [60] | |

e. | Guarantee of wind energy integration with power system | Liu et al. [8], Hong et al. [22], Zang et al. [52], Bao et al. [50], Zhang et al. [46], Xiaolan and Hui [59] | |

f. | Improvement in utilisation efficiency of wind power | Wang et al. [35], Tatinati and Veluvolu [21], Guo et al. [10], Yu et al. [37], Xiaolan and Hui [59] Ren and Suganthan [54], Wang et al. [33], Ren et al. [11], Wang et al. [44] | |

g. | Reduction in intergration and operation costs. | Wang et al. [35], Sun and Yuan [39], Zang et al. [52], Ren et al. [11], Bao et al. [50], An et al. [30] | |

h. | Sizing of energy storage capacity | Sun and Yuan [39], Sun et al. [27], Liu et al. [8], Dokur et al. [47], Xiaolan and Hui [59] | |

3 | Security | ||

a. | Ensure security, safety and stability of power system | Sun and Yuan [39], Hong et al. [7], Tatinati and Veluvolu [21], Sun et al. [27], Liang et al. [58] Ren and Suganthan [54], Liu et al. [48], Yu et al. [37], Liu et al. [55], Liu et al. [49] Bao et al. [50], Wang et al. [44], An et al. [30], Xingjie et al. [24], Liu et al. [25] | |

b. | Reduction of chances of wind power system collapse or breakdown | Liu et al. [48], Wang et al. [15], Liu et al. [36], Zhang et al. [46], Zhang et al. [12] | |

4 | Revenue Generation | ||

a. | Maintenence of controllable demand-supply equillibrium | Ren and Suganthan [54] | |

b. | Electricity bidding and trading | Dokur et al. [47], Wu and Peng [41], Wang et al. [15], Drisya and Kumar [29], Dejun et al. [60] Xingjie et al. [24], Xiaolan and Hui [59], Li and Wang [26] | |

5 | Other applications | ||

a. | To avoid accident calamities caused by derailment of trains | Liu et al. [23] |

Language | Package | Publication/Manual/Website Link |
---|---|---|

R | EMD | Kim and Oh [105] |

Rlibeemd | Luukko et al. [106] | |

Python | PyEMD | https://pypi.python.org/pypi/EMD-signal |

pyeemd | https://bitbucket.org/luukko/pyeemd.git | |

Matlab | EMD | https://goo.gl/zp8BG2 |

CEEMDAN | https://goo.gl/2Dp7d8 | |

Scilab | EMD Toolbox | https://atoms.scilab.org/toolboxes/emd_toolbox/1.3 |

Model | Liu et al. [8] (EMD-BPNN) | Ren et al. [9] (EMD-BPNN) | Hong et al. [7] (EMD-BPNN) | Mean | |||||
---|---|---|---|---|---|---|---|---|---|

Data | Speed | Speed | Speed (Spring) | Power (Spring) | - | ||||

Step size | 1 | 2 | 3 | 1 | 3 | 5 | 1 | 1 | - |

RMSE | 47.14 | 62.38 | 12.17 | 29.60 | 32.21 | 29.91 | 63.00 | 32.20 | 38.58 |

MAE | 42.25 | 59.29 | 7.49 | - | - | - | 53.60 | 22.13 | 36.35 |

MAPE | 42.01 | 55.75 | 29.75 | - | - | - | - | - | 42.50 |

Other Methodsunder comparison | ARIMA, Persistence method | Various combination of BPNN, AdaBoost and Regression tree | ARIMA, Persistence method | - |

**Table 5.**Improvement in EEMD-BPNN, EEMD-GABP model as compared to simple BPNN, EMD-BPNN and GABP, EMD-GABP, respectively.

Model | Ren et al. [11] (EEMD-BPNN) | Wang et al. [33] Wang et al. [34] (EEMD-GABP) | ||
---|---|---|---|---|

Data | Speed | Speed | ||

Step size | 1 | 3 | 5 | 1 |

Comparative method | BPNN | GABP | ||

RMSE | 16.66 | 5.39 | 6.10 | 62.17 |

MAPE | - | - | - | 59.57 |

Comparative method | EMD-BPNN | EMD-GABP | ||

RMSE | 44.44 | 33.55 | 33.94 | 25.31 |

MAPE | - | - | - | 25.95 |

Other methodsunder comparison | Various combinations of BPNN, SVR with EMD, EEMD, CEEMD, CEEMDAN | WNN |

Model | Wang et al. [35] (EMD-ENN) | Mean | |||
---|---|---|---|---|---|

Data | Speed | - | |||

Season | Spring | Summer | Fall | Winter | - |

RMSE | 4.12 | 16.07 | 46.34 | 16.66 | 20.79 |

MAE | 13.5 | 9.85 | 33.92 | 16.48 | 18.43 |

MAPE | 15.62 | 24.00 | 47.82 | 35.29 | 30.68 |

Other methods under comparison | Presistance method, BPNN |

Model | Liu et al. [36] (EEMD-ENN) | Yu et al. [37] (EEMD-ENN) | Mean | ||||
---|---|---|---|---|---|---|---|

Data | Speed | Speed | - | ||||

Step size | 1 | 2 | 3 | 1 | 2 | 3 | - |

RMSE | 79.35 | 68.68 | 56.81 | 53.71 | 53.99 | 57.75 | 61.71 |

MAE | 79.23 | 66.52 | 55.00 | 52.71 | 52.83 | 54.25 | 60.09 |

MAPE | 78.95 | 63.57 | 50.82 | 50.35 | 52.12 | 65.20 | 52.66 |

Other methods under comparison | MPL-NN, ARIMA | Persistance method, ARIMA | - |

Model | Zheng et al. [38] EMD-RBFNN | Wang et al. [44] EEMD-FNN | Zhang et al. [46] EEMD-WNN | Dokur et al. [47] EMD-LMNN | Zhang et al. [14] EMD-LMNN | Mean |
---|---|---|---|---|---|---|

Data | Power | Speed | Speed | Speed | Speed | - |

Comparative Method | RBFNN | FNN | WNN | LMNN | LMNN | - |

Step size | 1 | 1 | 1 | 1 | 1 | - |

RMSE | 42.11 | 50.00 | 67.27 | 69.38 | 36.00 | 52.95 |

MAE | 36.75 | 44.68 | 68.40 | 45.87 | 32.60 | 45.66 |

MAPE | 40.48 | 40.73 | 66.59 | - | 28.76 | 44.14 |

Other methodsunder comparison | - | - | BPNN, RBFNN | - | SVM | - |

Model | Liu et al. [49] FEEMD-MLP | Liu et al. [48] FEEMD-MEA-MLP | Mean | ||||
---|---|---|---|---|---|---|---|

Data | Speed | Speed | - | ||||

Step size | 1 | 2 | 3 | 1 | 2 | 3 | - |

RMSE | 68.50 | 65.20 | 68.76 | 67.52 | 71.61 | 72.83 | 69.07 |

MAE | 68.37 | 65.60 | 69.43 | 67.71 | 75.18 | 77.62 | 70.65 |

MAPE | 67.26 | 66.01 | 69.39 | 68.69 | 77.40 | 80.32 | 71.51 |

Other methods under comparison | ARIMA, ANFIS | FEEMD-GA-MLP | - |

Model | Liu et al. [55] | Sun and Liu [56] | Mean | |||
---|---|---|---|---|---|---|

Data | Speed | Speed | - | |||

Step size | 1 | 2 | 3 | 1 | 1 | - |

Location | - | - | - | 1 | 2 | - |

Method | EMD-ELM | EMD-RELM | - | |||

RMSE | 50.83 | 63.04 | 65.79 | - | - | 59.88 |

MAE | 47.34 | 62.65 | 63.13 | 11.46 | 5.09 | 37.93 |

MAPE | 48.02 | 63.79 | 64.13 | 43.30 | 38.07 | 51.46 |

Method | FEEMD-ELM | FEEMD-RELM | - | |||

RMSE | 70.94 | 69.39 | 71.16 | - | - | 70.49 |

MAE | 71.46 | 70.17 | 70.75 | 37.75 | 21.32 | 54.29 |

MAPE | 70.75 | 71.09 | 71.80 | 31.52 | 21.84 | 53.40 |

Model | Han and Zhu [17] EMD-SVM | Lin and Peng [18] EMD-SVM | Zhang et al. [16] EMD-SVM | Zhang et al. [14] EMD-SVM | Wang et al. [15] EMD-SVM | Mean | ||||
---|---|---|---|---|---|---|---|---|---|---|

Data | Speed | Power | Power | Speed | Speed | - | ||||

Step Size | - | - | - | one step | One | Three | Five | - | ||

Location | - | - | - | 1 | 2 | 3 | - | - | - | - |

%RMSE | - | 20.76 | 55.84 | 36.29 | 40.44 | 11.95 | 36.22 | 35.55 | 34.64 | 33.96 |

%MAE | 39.54 | - | - | 34.06 | 38.23 | 08.40 | - | - | - | 30.05 |

%MAPE | - | 31.63 | - | 28.51 | 36.31 | 05.49 | - | - | - | 25.48 |

Other methodsunder comparison | - | - | - | ANN, DFA-ANN, DFA-SVM, DSF-ANN, DSF-SVM | ANN, BPNN, ENN, WNN | - |

Model | Jia [20] EEMD-SVM | Hu et al. [19] EEMD-SVM | Ren et al. [11] EEMD-SVM | Mean | ||||
---|---|---|---|---|---|---|---|---|

Data | Speed | Speed | Speed | - | ||||

Step Size | one | one | one | one | three | five | - | |

Location | - | 1 | 2 | - | - | - | - | |

SVM | RMSE | - | - | - | 18.87 | 35.48 | 37.75 | 30.7 |

MAE | - | 51.61 | 17.30 | - | - | - | 34.45 | |

MAPE | 11.79 | 53.85 | 13.66 | 17.60 | 37.86 | 31.78 | 27.75 | |

EMD-SVM | RMSE | - | - | - | 16.89 | 18.48 | 12.57 | 15.98 |

MAE | - | 21.05 | 20.37 | - | - | - | 20.71 | |

MAPE | - | 27.45 | 19.49 | 21.11 | 22.04 | 19.36 | 21.89 | |

Other methodsunder comparison | EMD-ARIMA, Persistence | ARIMA, SARIMA | Persistence | - |

Model | Hong et al. [22] | Liang et al. [58] | Dejun et al. [60] | Xiaolan and Hui [59] | Mean | ||||
---|---|---|---|---|---|---|---|---|---|

Methods | EMD-LSSVM,Polyregression | EMD-LSSVM | EMD-LSSVM-ELM | EMD-WT-LSSVM | EMD-LSSVM | - | |||

Data | Power (indirect) | Power | Speed | Speed | - | ||||

Step size | one | two | three | four | six | six | one | one | - |

RMSE | 40.16 | 34.53 | 36.21 | 40.81 | 18.51 | 49.44 | 81.30 | 20.33 | 40.53 |

MAE | 42.13 | 37.96 | 39.57 | 41.66 | 23.56 | 44.29 | - | - | 38.19 |

MAPE | - | - | - | - | - | - | 72.34 | 27.66 | 50.00 |

Other methodsunder comparison | MTD/MMLP, Persistence | ELM, EMD-ELM | - | EMD-RLS | - |

**Table 14.**Improvement in modified EEMD-LSSVM models as compared to simple LSSVM and EMD-LSSVM models.

Model | Jiang and Huang [40] EEMD-LSSVM | Wu and Peng [41] EEMD-LSSVM | Safari et al. [42] EEMD-LSSVM | Mean | ||
---|---|---|---|---|---|---|

Data | Speed | Power | Power | - | ||

Step size | one | one | six | Twelve | - | |

LSSVM | RMSE | 13.93 | 45.25 | 47.79 | 49.68 | 39.16 |

MAE | 13.26 | 54.36 | 47.13 | 11.55 | 31.57 | |

MAPE | - | 41.64 | - | - | 41.64 | |

EEMD-LSSVM | RMSE | 22.49 | 31.29 | 7.87 | 18.67 | 20.08 |

MAE | 21.66 | 39.52 | 2.32 | 3.47 | 16.74 | |

MAPE | - | 20.98 | - | - | 20.98 | |

Other methodsunder comparison | - | ARIMA, BPNN, PCA, BA, PSO | Persistence, RBFNN | - |

Model | Zang et al. [52] EEMD-RVM | Zang et al. [53] EEMD-RVM |
---|---|---|

Data | Speed | Power |

RMSE | 16.33 | - |

MAPE | 4.58 | 51.06 |

Other methods under comparison | BPNN, SVM | ELM |

Model | Liu et al. [23] EMD-ARMA | Liu et al. [25] EMD-ARMA | Li and Wang [26] EMD-ARMA | |||
---|---|---|---|---|---|---|

Data | Speed | Speed | Speed | |||

Step size | 1 | 3 | 5 | 1 | 1 | |

ARMA | RMSE | - | - | - | 39.45 | - |

MAE | 50.40 | 64.15 | 75.41 | - | - | |

MAPE | 50.00 | 63.81 | 75.76 | - | 16.49 | |

Other methodsunder comparison | ARIMA, PRWM, BPNN | ARMA, Persistance | ARMA |

Model | Zhang et al. [28] EEMD-ANFIS-SARIMA | Mean | ||||
---|---|---|---|---|---|---|

Location/Date | Site 1 28 February 2016 | Site 2 28 February 2016 | - | |||

Prediction Horizon | 3 h | 24 h | 3 h | 24 h | - | |

SARIMA | RMSE | 65.21 | 66.66 | 65.85 | 70.00 | 66.93 |

MAE | 66.66 | 64.28 | 64.70 | 65.00 | 65.15 | |

MAPE | 64.45 | 63.93 | 72.11 | 68.52 | 67.25 |

Model | Ren and Suganthan [54] EMD-kNN-M | ||||
---|---|---|---|---|---|

Data | Speed | ||||

Comparative Methods | kNN | EMD-kNN-P | Mean | ||

Step size | 1 | 3 | 1 | 3 | - |

RMSE | 18.91 | 13.08 | 14.88 | 14.17 | 15.26 |

MAPE | 18.15 | 04.01 | 18.85 | 11.72 | 13.18 |

Other methods under comparison | Persistence model |

Model | Bokde et al. [45] EEMD-PSF | Mean | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

Data | Speed | ||||||||||

Comparative method | PSF | ARIMA | EMD-PSF | EMD-ARIMA | EEMD-ARIMA | - | |||||

Step size | 12 | 24 | 12 | 24 | 12 | 24 | 12 | 24 | 12 | 24 | - |

RMSE | 19.14 | 20.00 | 71.18 | 75.51 | 69.61 | 74.10 | 51.42 | 64.00 | 45.16 | 46.26 | 53.63 |

MAE | 26.31 | 14.70 | 72.27 | 79.57 | 71.42 | 76.94 | 57.57 | 68.81 | 51.72 | 45.28 | 56.45 |

MAPE | 25.83 | 25.78 | 70.50 | 71.04 | 69.42 | 67.36 | 55.37 | 42.57 | 49.87 | 42.57 | 52.02 |

**Table 20.**Combinations of Prediction error measures (RMSE, MAPE and MAE) used in articles using hybrid EMD/EEMD models for wind data.

Articles | RMSE | MAPE | MAE |
---|---|---|---|

An et al. [30], Bao et al. [50], Wang et al. [44], Liu et al. [48], Sun et al. [43], Liu et al. [36], Liu et al. [55] Liu et al. [49], Zhang et al. [14], Wu and Peng [41], Zhang et al. [28], Zhang et al. [46], Zhang et al. [12], Yu et al. [37] | √ | √ | √ |

Xiaolan and Hui [59], Dejun et al. [60], Lin and Peng [18], Sun et al. [27], Sun and Yuan [39], Wang et al. [33], Ren et al. [13], Wang et al. [32], Zang et al. [52], Wang et al. [15], Wang et al. [34], Zang et al. [52] | √ | √ | |

Guo et al. [10], Liu et al. [8], Zheng et al. [38], Hu et al. [19], Tatinati and Veluvolu [21], Wang et al. [35], Sun and Liu [56] | √ | √ | |

Hong et al. [7], Zhang Xue-Qing [31], Liang et al. [58], Hong et al. [22], Safari et al. [42], Jiang and Huang [40] | √ | √ | |

Ren and Suganthan [54], Ren and Suganthan [54], Zhang et al. [16], Ren et al. [11], Drisya and Kumar [29] | √ | ||

Li and Wang [26], Jia [20], Fei [51] | √ | ||

Xingjie et al. [24], Dokur et al. [47] | √ |

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**MDPI and ACS Style**

Bokde, N.; Feijóo, A.; Villanueva, D.; Kulat, K.
A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. *Energies* **2019**, *12*, 254.
https://doi.org/10.3390/en12020254

**AMA Style**

Bokde N, Feijóo A, Villanueva D, Kulat K.
A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. *Energies*. 2019; 12(2):254.
https://doi.org/10.3390/en12020254

**Chicago/Turabian Style**

Bokde, Neeraj, Andrés Feijóo, Daniel Villanueva, and Kishore Kulat.
2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction" *Energies* 12, no. 2: 254.
https://doi.org/10.3390/en12020254