Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis
Abstract
:1. Introduction
2. Data Acquisition
2.1. Data Introduction
2.2. Meteorological Data
3. Methodology
3.1. Noise Reduction of Wind Power Generation
- Add Gaussian white noise to the signal to be decomposed y(t), where t is the time. Obtain a new signal , where is the number of white noise experiments, is Gaussian white noise that satisfies a standard normal distribution, is the total number of modal components obtained by decomposition and can be obtained by referring to the standard table of white noise.
- The first intrinsic mode function of CEEMDAN is obtained by the overall average of the generated mode functions.
- The following is used to calculate the residual after removing the first mode function.
- A new signal is obtained by adding Gaussian white noise with equal positive and negative values to . EMD is carried out with the new signal as the carrier to obtain the first-order mode function , so as to obtain the second intrinsic mode function of CEEMDAN.
- The following is used to calculate the residual after removing the second mode function.
- The above steps are repeated until the obtained residual signal becomes a monotonic function and cannot be decomposed, and the algorithm ends. The number of intrinsic mode functions obtained is , and the original signal is decomposed into:
3.2. Data Preprocessing
3.2.1. Wind Power Output Data Missing or Abnormal
3.2.2. Variable Scale of Meteorological Data
3.2.3. Feature Scaling
3.3. LightGBM Algorithm
3.4. Selection of Input Features
3.4.1. Autocorrelation Analysis of Wind Power
3.4.2. Meteorological Feature Selection via the Maximal Information Coefficient
3.4.3. Model Structure
3.5. Nonparametric Regression Based on the Gaussian Kernel Function
- The prediction error of wind power is the deviation between the predicted output and real output.
- Assuming that the probability distribution curve fitted by is , the symmetrical probability interval shall be adopted in the calculation process. That is, if the predicted wind power is and the probability is , the interval shall be:
3.6. Evaluation Criteria of the Models
3.7. Overview of Framework
- Noise reduction is carried out on the historical output data of wind power to improve the validity of the data, and the autocorrelation analysis of daily average wind power output is carried out.
- Meteorological data are downloaded from ERA5 and preprocessed.
- All data are normalized.
- MIC is used to analyze the correlation between meteorological data and wind power data, and the meteorological data with high correlation are selected as the input.
- The output prediction is made under different input structures.
- The normalized wind power output is restored to the original level.
- Based on the prediction error, the probability distribution function is calculated based on nonparametric regression.
- The prediction interval of wind power output is obtained by combining probability distribution function.
4. Experimental Results and Discussion
4.1. Feature Selection
4.2. Analysis of Prediction Results
4.3. Impact and Comparison of Meteorological Input
4.4. Wind Power Interval Prediction and Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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No. | Variable | Description | Units |
---|---|---|---|
1 | u100 | West wind at 100 m elevation | m · s−1 |
2 | v100 | South wind at 100 m elevation | m · s−1 |
3 | u10n | West wind of neutral wind at 10 m elevation | m · s−1 |
4 | u10 | West wind at 10 m elevation | m · s−1 |
5 | v10n | South wind of neutral wind at 10 m elevation | m · s−1 |
6 | v10 | South wind at 10 m elevation | m · s−1 |
7 | fg10 | 10 m wind gust since previous post-processing (since the parameter was last archived in a particular forecast) | m · s−1 |
8 | d2m | 2 m dewpoint temperature | K |
9 | t2m | 2 m temperature | K |
10 | i10fg | Instantaneous 10 m wind gust | m · s−1 |
11 | cdir | Clear-sky direct solar radiation at surface | J · m−2 |
12 | e | Evaporation | mm |
13 | mx2t | Maximum 2 m temperature since previous post-processing | K |
14 | megwss | Mean eastward gravity wave surface stress | N · m−2 |
15 | mgwd | Mean gravity wave dissipation | W · m−2 |
16 | mngwss | Mean northward gravity wave surface stress | N · m−2 |
17 | mn2t | Minimum 2 m temperature since previous post-processing | K |
18 | skt | Skin temperature | K |
19 | es | Snow evaporation | mm |
20 | stl1 | Soil temperature level 1 | K |
21 | slhf | Surface latent heat flux | J · m−2 |
22 | sp | Surface pressure | Pa |
23 | p59 | Mean eastward turbulent surface stress | N · m−2 |
No | Variable | MIC | Units |
---|---|---|---|
1 | u10 | 0.694 | m · s−1 |
2 | u10n | 0.682 | m · s−1 |
3 | fg10 | 0.662 | m · s−1 |
4 | u100 | 0.645 | m · s−1 |
5 | i10fg | 0.640 | m · s−1 |
6 | t2m | 0.583 | K |
7 | mgwd | 0.577 | W · m−2 |
8 | megwss | 0.543 | N · m−2 |
9 | sp | 0.523 | Pa |
10 | d2m | 0.483 | K |
11 | v10 | 0.467 | m · s−1 |
12 | v10n | 0.462 | m · s−1 |
13 | v100 | 0.435 | m · s−1 |
14 | mx2t | 0.405 | K |
15 | mngwss | 0.374 | N · m−2 |
16 | mn2t | 0.364 | K |
17 | skt | 0.352 | K |
18 | es | 0.343 | m of water equivalent |
19 | stl1 | 0.274 | K |
20 | slhf | 0.258 | J · m−2 |
21 | p59 | 0.189 | N · m−2 |
22 | cdir | 0.154 | J · m−2 |
23 | e | 0.102 | m of water equivalent |
No | Input |
---|---|
1 | , |
2 | |
3 | |
4 | |
5 | |
6 | |
7 | |
8 | |
9 | |
10 | |
11 | |
12 | |
13 | |
14 | |
15 | |
16 | |
17 | |
18 | |
19 | |
20 | |
21 | |
22 | |
23 |
No. | Description | Index | Unit | MIC | Type |
---|---|---|---|---|---|
1 | power on day d-1 | W | - | Obs | |
2 | power on day d-2 | W | - | Obs | |
3 | power on day d-3 | W | - | Obs | |
4 | power on day d-4 | W | - | Obs | |
5 | 10 m u-component of wind | u10 | m · s−1 | 0.694 | ERA5 |
6 | 10 m u-component of neutral wind | u10n | m · s−1 | 0.682 | ERA5 |
7 | 10 m wind gust since previous post-processing | fg10 | m · s−1 | 0.662 | ERA5 |
8 | 100 m u-component of wind | u100 | m · s−1 | 0.645 | ERA5 |
9 | Instantaneous 10 m wind gust | i10fg | m · s−1 | 0.640 | ERA5 |
10 | 2 m temperature | t2m | K | 0.583 | ERA5 |
11 | Mean gravity wave dissipation | mgwd | W · m−2 | 0.577 | ERA5 |
12 | Mean eastward gravity wave surface stress | megwss | N · m−2 | 0.543 | ERA5 |
13 | Surface pressure | sp | Pa | 0.523 | ERA5 |
14 | 2 m dewpoint temperature | d2m | K | 0.483 | ERA5 |
15 | 10 m v-component of wind | v10 | m · s−1 | 0.467 | ERA5 |
Parameter | Screening Scope | Selected Result |
---|---|---|
Learning rate | 0.1, 0.2, …, 0.6 | 0.1 |
Feature fraction | 0.5, 0.6, …, 1.0 | 0.8 |
Num leaves | 8, 16, 32, 64, 128 | 16 |
Max depth | 1, 2, …, 9 | 3 |
Model | RMSE (MW) | MAE (MW) | CORR | KGE | IA | |
---|---|---|---|---|---|---|
train | LightGBM-MIC | 373 | 287 | 0.940 | 0.898 | 0.967 |
XGBoost-MIC | 386 | 301 | 0.932 | 0.874 | 0.966 | |
RF-MIC | 472 | 359 | 0.922 | 0.870 | 0.954 | |
SVR-MIC | 584 | 452 | 0.904 | 0.839 | 0.938 | |
test | LightGBM-MIC | 551 | 425 | 0.927 | 0.869 | 0.959 |
XGBoost-MIC | 584 | 437 | 0.917 | 0.853 | 0.952 | |
RF-MIC | 620 | 471 | 0.908 | 0.840 | 0.947 | |
SVR-MIC | 678 | 541 | 0.879 | 0.814 | 0.926 |
Model | Input | RMSE | MAE | CORR | KGE | IA |
---|---|---|---|---|---|---|
LightGBM | Four traditional meteorological characteristics | 734 | 536 | 0.863 | 0.806 | 0.907 |
LightGBM-MIC | Eleven meteorological characteristics | 551 | 425 | 0.927 | 0.869 | 0.959 |
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Liao, S.; Tian, X.; Liu, B.; Liu, T.; Su, H.; Zhou, B. Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis. Energies 2022, 15, 6287. https://doi.org/10.3390/en15176287
Liao S, Tian X, Liu B, Liu T, Su H, Zhou B. Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis. Energies. 2022; 15(17):6287. https://doi.org/10.3390/en15176287
Chicago/Turabian StyleLiao, Shengli, Xudong Tian, Benxi Liu, Tian Liu, Huaying Su, and Binbin Zhou. 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis" Energies 15, no. 17: 6287. https://doi.org/10.3390/en15176287