A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction
Abstract
:1. Introduction
2. Performance Evaluation Metrics
- Mean absolute error (MAE):MAE is average value of absolute different between predicted and actual value.
- Root Mean Square Error (RMSE):RMSE computes the standard deviation of the residuals between predicted and actual values. Residuals defined the distance between regression line data points and RMSE measures the spread of these residuals.
- Mean Square Error (MSE):The mean squared error calculates the average of the squares of the error in the prediction.
- Mean Absolute Percentage Error (MAPE):The mean absolute percentage error (MAPE) is average of the absolute percentage error in the forecasts.
- Normalised RMSE (NRMSE):Normalization of the RMSE value is useful for fair comparison of the model on different scales. The normalization can be performed with respect to mean or standard deviation. The following is the Mean NRMSE.
- Normalized Mean Absolute Error (NMAE):NMAE is used to compare the MAE of models with different scales. The NMAE is a two-step process. The normalization can be performed with respect to mean, range or inter quartile range. The following is Range NMAE.
- Root Mean Square Prediction Error (RMSPE):RMSPE calculates the root mean squared percentage error regression loss.
- R-Square ():is the coefficient of determination, it computes the variance of the prediction from the measured data. A negative value of implies worse prediction while it can reach a maximum value of 1.
3. Long Term Prediction
3.1. Time Series Analysis
3.2. Machine Learning
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Fang et al., 2016 [28] | Improved GPR | NRMSE | 0.14564 |
2 | Tian et al., 2016 [25] | CGNN | MSE | 0.004 |
3 | Tinghui et al., 2017 [26] | Swinging door+SVM | Precision, Recall Accuracy, Error | 0.8059, 0.8390 0.8747, 0.1253 1 |
4 | Zhongda et al., 2018 [15] | Modified SVM | RMSE MAE MAPE | 59.313 50.344 0.038 |
5 | Bogdan et al., 2021 [29] | Comparison of ML | RMSE MAPE | 412 0.267 2 |
6 | Huaiping et al., 2021 [27] | ensemble of mixture of GPR | RMSE R² | 1.7771 0.9057 3 |
3.3. Deep Learning Models
3.4. Hybrid Approach
4. Short-Term Prediction
4.1. Machine Learning
4.2. Deep Learning Methods
4.3. Hybrid Methods
5. Ultra Short-Term Wind Power Prediction
5.1. Machine Learning
5.2. Deep Learning
5.3. Hybrid Methods
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ANFIS | Adaptive Neuro-Fuzzy Inference System |
ANN | Artificial Neural Network |
AR | Autoregressive |
ARX | Autoregressive with exogenous variable |
ARIMA | Autoregressive Integrated Moving Average |
ARMA | Autoregressive Moving Average |
AWNN | Adaptive Wavelet Neural Network |
BP | Back Propagation |
BPNN | Back-Propagation Neural Network |
CNN | Convolutional Neural Network |
DBN | Deep Belief Network |
ELM | Extreme Learning Machine |
ENN | Elman Neural Network |
EVS | Explained Variance Score |
FFNN | Feed Forward Neural Network |
GA | Genetic Algorithm |
GFS | Global Forecasting System |
GMM | Gaussian Mixture Model |
GP | Gaussian Process |
GPR | Gaussian Process Regression |
IF | Isolation Forest |
LSSVM | Least Square Support Vector Machine |
LSTM | Long Short-Term Memory |
MA | Moving Average |
MAPE | Mean Absolute Percentage Error |
MARE | Mean Absolute Relative Error |
MDN | Mixture Density Neural Network |
MLP | Multilayer Perceptron |
MSE | Mean Square Error |
MSLE | Mean Squared Logarithmic Error |
NAAE | Normalised Absolute Average Error |
NMAE | Normalised Mean Absolute Error |
NMBE | Normalized Mean Bias Error |
NMSE | Normalized Mean Square Error |
NN | Neural Network |
NRMSE | Normalised Root Mean Square Error |
NWP | Numerical Weather Prediction |
PSO | Particle Swarm Optimisation |
R-Square | |
RBFNN | Radial Basis Function Neural Network |
RF | Random Forest |
RMSE | Root Mean Square Error |
RVM | Relative Vector Machine |
SDE | Standard Deviation Error |
SNMAE | Square Normalized Mean Bias Error |
SVM | Support Vector Machine |
SVR | Support Vector Regression |
WNN | Wavelet Neural Network |
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Prediction Horizon | Time Range |
---|---|
Long term | a day to 6 days ahead |
short-term | an hour to a day ahead |
Ultra short-term | 5 min to 1 h |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1. | Karakus et al., 2017 [20] | Polynomial AR | NRMSE NMAPE | 0.1199 6.8146 |
2 | Ouyang et al., 2019 [22] | Residue correction | MAE RMSE Bias | 4.9647 8.9453 0.0013 |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Abedinia et al., 2017 [30] | autoencoder features and SVR | PE | 0.152 1 |
2 | Abedinia et al., 2020 [31] | 2D CNN | MAPE NRMSE | 5.93% 9.40% |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Dong et al., 2016 [33] | NWP + GRNN | NMAE NRMSE | 10.67 14.01% |
2 | Shi et al., 2018 [35] | LSTM + VMD | RMSE MAE MAPE | 6.44 7.16 22% |
3 | Abedinia et al., 2020 [34] | BaNN + K-means clustering | MAPE NRMSE | 2.71 19.78% |
4 | Zhang et al., 2021 [32] | E-EMD + RNN | MAE, SDE RMSE, MAPE | 144.79, 60.32 81.77, 12.79% |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Heinermann et al., 2016 [36] | SVR | MSE | 588.60 |
2 | Yan et al., 2016 [38] | Variant of GPR | NRMSE MAE | 0.05 0.1 |
3 | Lahouar et al., 2017 [40] | random forests | MAE,RMSE NAME,MAPE,MASE | 0.93, 1.76 1.72, 10.29, 0.4 1 |
4 | Wang et al., 2018 [41] | Nonlinear PLS | RMSECV RMSEP | 206.2611 225.5973 |
5 | Liu et al., 2018 [39] | GPR with data imputation | RMSE MAE | 0.9763 0.8321 2 |
6 | Xie et al., 2018 [48] | Bayesian Framework | Skill score Coverage | −5.314 0.164 |
7 | Wang et al., 2019 [55] | Small-world NN | eNRMSE eNMAE | 7.5 4.9 |
8 | Zhang et al., 2019 [47] | PSO-SVR | MAE RMSE MAPE | 76.9 109.4 15.7% |
9 | Jianqi et al., 2020 [44] | Weighted Naive Bayes | MAE NRMSE | 8.97 77.23% |
10 | Junho 2020 [37] | Boosted Trees + RF | MAE RMSE | 22.54 50.44 98.88 |
11 | Ning et al., 2020 [42] | Kernel ELM | MAE RMSE MSE | 7.30 10.22 104.59 0.53 |
12 | Guoqing et al., 2021 [43] | Adaboost | MAE, MSE RMSE, MAPE | 23.38, 1023.4 31.99, 0.028 0.99 |
13 | Ghoushchi et al., 2021 [54] | fuzzy Wavenet | RMSE MAPE | 0.198 0.00123 |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Qureshi et al., 2017 [56] | DNN with transfer learning | MAE SDE RMSE | 0.0658 0.0929 0.0939 1 |
2 | Wang et al., 2018 [57] | Deep belief network | NMAE NRMSE | 0.0236 0.0322 |
3 | Ding et al., 2019 [59] | Gated RUNN | RMSE MAE | 13.45 6.87 2 |
4 | Zhang et al., 2020 [58] | Deep Mixture Density Network | NMAE NRMSE | 0.108 0.147 |
5 | Wang et al., 2021 [61] | Multidimensional data cleaning + feature reconfiguration | MAE, MAPE NRMSE | 2.18, 4.36% 7.29% |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value |
---|---|---|---|---|
1 | Liang et al., 2016 [76] | SVM + ELM | NMAE, NRMSE | 0.0417, 0.0621 |
2 | Cornejo et al., 2017 [63] | Hybrid ML Methods | RMSE, MAE | 5.3066, 3.9519 |
3 | Huang et al., 2017 [70] | SVM + k-means clustering | RMSE, MRE | 57.0628, 2.0112% |
4 | Yuan et al., 2017 [71] | Hybrid AR + LSSVM | RMSE, MAPE, MAE | 114.80, 8.33%, 85.56 |
5 | Safari et al., 2017 [14] | EEMD + SSA + LSSVM | NRMSE NMAE | 5.9671 3.4262 |
6 | Naik et al., 2018 [68] | EMD + KRR | RMSE, MAPE | 1.0674, 7.68% |
7 | Zheng et al., 2018 [72] | GA + PSO+ ANFIS | MAE, NMAE, MAPE | 45.73, 1.83, 6.64 |
8 | Liu et al., 2019 [77] | LSTM + wavelet | MAE, MAPE, RMSE | 10.12, 3.01%, 14.22 |
9 | Son et al., 2019 [78] | LSTM + ANN | RMSE, MAPE | 3.67, 5.04% |
10 | Mishra et al., 2019 [79] | ANN + RBF | MAPE, RMSE | 8.1043, 0.8394 |
11 | Zhang et al., 2019 [80] | CEEMD + LLE + ELM | NRMSE, NMAE, R | 7.372, 6.124 91.958 |
12 | Shahid et al., 2020 [69] | LSTM + Wavelet | MSE, MAE MAER, MAPE, | 0.0089, 0.0644 0.0416, 0.4512, 0.9221 |
13 | Shahid et al., 2020 [69] | GP + ANNs + Axp-GPNN | RMSE, SDE, MAE | 0.0845, 0.0580, 0.0841 |
14 | Zhao et al., 2020 [81] | SSA+ Temporal CNN | RMSE, SMAPE, | 188.79, 23.07, 0.9804 |
15 | Yan et al., 2020 [82] | EWT + KLEM + GRUNN | RMSE, MAE, GRA | 33.75, 27.44 0.95 |
16 | Liu et al., 2020 [83] | Wavelet + LSTM | MAE, MAPE, RMSE | 49.896, 5.831, 63.991 |
17 | Han et al., 2020 [84] | wavelet WPD + VMD + SSA | NMAE, NRMSE, MAPE, | 0.66, 0.84, 2.3, 0.99 |
18 | Duan et al., 2021 [85] | VMD + LSTM neural network | MAE,RMSE,TIC | 41.10, 58.77, 0.0047 |
19 | Shahid et al., 2021 [62] | LSTM + GA | MSE, MAE, RMSE, EV, R2Score | 0.00924, 0.87413, 0.09615 0.07271, 0.87656 |
20 | Huang et al., 2022 [86] | VMD + BiLSTM–CNN–WGAN GP | MAE, MAPE, RMSE | 0.28, 1.26%, 0.33 |
21 | Zhang et al., 2022 [74] | DWT + SARIMA + LSTM | Prediction Accuracy | 0.99 |
22 | Hanifi et al., 2022 [75] | ARIMA + LSTM | RMSE | 484.3 |
Sr. No. | Articles | Models | Evaluation Metric | Evaluation Value | Prediction Horizon |
---|---|---|---|---|---|
1 | Yesilbudak et al., 2017 [89] | KNN classifier | , | 2.475%, 15.839 1.158% | 10 min |
2 | Liu et al., 2018 [87] | PSR and MLR | NMAE | 5% | less than 4 h |
3 | Yang et al., 2018 [92] | IEMD + ANN + SVM | NRMSE DMAP DMQP | 9.21% 93.82% 91.91% | 4 h |
4 | Dong et al., 2018 [73] | mRMR + IVS | NRMSE, NMAE | 6.67, 4.10 | 1 h to 4 h |
5 | Tan et al., 2020 [88] | SSA_ELM | MAE MAPE RMSE | 0.09 0.47 0.16 | 15 min to 4 h |
6 | Sun et al., 2020 [93] | LSTM + WT | MAPE, RMSE | 1.54%, 3.66 | 2 h |
7 | Hossain et al., 2021 [94] | LSTM + GRUNN | MAE, RMSE, MAPE | 2.45, 3.85, 9.80% | 5 min |
8 | Hossain et al., 2021 [95] | CNN + GRU + LSTM | MAPE | 17.9 | 5 min and 10 min |
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Sawant, M.; Patil, R.; Shikhare, T.; Nagle, S.; Chavan, S.; Negi, S.; Bokde, N.D. A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction. Energies 2022, 15, 8107. https://doi.org/10.3390/en15218107
Sawant M, Patil R, Shikhare T, Nagle S, Chavan S, Negi S, Bokde ND. A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction. Energies. 2022; 15(21):8107. https://doi.org/10.3390/en15218107
Chicago/Turabian StyleSawant, Manisha, Rupali Patil, Tanmay Shikhare, Shreyas Nagle, Sakshi Chavan, Shivang Negi, and Neeraj Dhanraj Bokde. 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction" Energies 15, no. 21: 8107. https://doi.org/10.3390/en15218107
APA StyleSawant, M., Patil, R., Shikhare, T., Nagle, S., Chavan, S., Negi, S., & Bokde, N. D. (2022). A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction. Energies, 15(21), 8107. https://doi.org/10.3390/en15218107