Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction
Abstract
:1. Intro
1.1. Organization of the Paper
1.2. Data Analysis
2. Introduction
2.1. Background and Motivation
2.2. Literature Review
2.2.1. General Model and Method of Short-Term Wind Power Prediction
2.2.2. Application of the LSTM Model in Short-Term Wind Power Prediction
2.3. Proposed Method and Contributions
3. Model and Method
3.1. Affine Optimization Correction Model for Wind Power Data
3.1.1. Model Introduction and Parameter Setting
3.1.2. Model Optimization
3.2. PSO-SWLSTM Prediction Model of Wind Power
3.2.1. Model Introduction
3.2.2. Simulation Steps and Parameter Settings
- (1)
- Normalize the processed data to facilitate subsequent data processing and map the data to 0~1 for processing.
- (2)
- Select the hyperparameters (number of neurons and learning rate) that need to be adjusted in LSTM and their respective optimization ranges.
- (3)
- Initialize PSO parameters. This includes the initial velocity, position, training frequency, and particle scale. The number of particle training is set to 20, and the number of particles is set to 100.
- (4)
- Set the inertia weight of PSO as 0.8 to ensure faster convergence speed of the particle swarm optimization algorithm.
- (5)
- Determine the fitness function of particles. In this paper, the RMSE value of the SWLSTM prediction model will be minimized as the fitness function of particles, and the optimal model parameters will be sought.
- (6)
- Set the step size of the model’s self-moving window. The SWLSTM model takes an eight step size as the window to predict step by step.
- (7)
- Calculate the fitness value of particles and update the optimal fitness value.
- (8)
- Record the optimal position of particles and update the optimal part of the population.
- (9)
- Determine whether the number of cycles is reached. If the number of cycles is satisfied, the optimal position of the particle swarm and SWLSTM prediction results are output. If not, return to Step 7.
3.3. Wind Power Error Feedback Correction Model Based on LSTM
3.3.1. Model Introduction
3.3.2. Simulation Process and Parameter Setting
4. Model Simulation and Result Analysis
4.1. Affine Optimization Model Results
4.2. Parameter Optimization Result
4.3. Comparative Analysis of Prediction Results
- (1)
- Compared with the traditional LSTM model, the PSO-SWLSTM model proposed in this paper can reduce the root mean square error of prediction results and obtain more reasonable predictions.
- (2)
- We can see that by comparing M3 and M4 curves with the M1 curves. The affine optimization model and EFCM-LSTM model proposed in this paper can effectively reduce the error of prediction results in data correction or error correction. This makes the forecast more efficient and reasonable.
- (3)
- It can be seen from the observation curves M3 and M4 that the improved model has good stability, and the RMSE value of the predicted result is stable at about 15%, which can improve the model prediction result stably and effectively.
- (4)
- Compared with the traditional LSTM model, the model proposed in this paper can significantly improve the prediction results and control the RMSE value at about 10%. The best result in 10 training can reach 7.52%.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
IEEE | Institute of Electrical and Electronics Engineers |
PSO-SWLSTM | Self-moving window long and short-term memory neural network prediction model based on particle swarm optimization |
EFCM-LSTM | Error feedback correction model based on the LSTM network |
MW | Megawatt |
p. u. | Per unit |
pcs | Pieces |
ρ | Represents the correlation between meteorological factors and wind power |
xt | Time series data of meteorological factors |
yt | Time series data of wind power |
The mean of the time series of meteorological factors | |
The mean value of wind power time series | |
V10 | Wind speed 10 m above the ground at any given time |
V30 | Wind speed 30 m above the ground at any given time |
V50 | Wind speed 50 m above the ground at any given time |
V70 | Wind speed 70 m above the ground at any given time |
T | The temperature of the atmosphere at a given time |
F | The humidity of the atmosphere at a given time |
P | Atmospheric pressure at a given time |
ε1 | Noise element introduced by prediction error. |
ε2 | Noise elements are introduced by other factors affecting the calculation error of wind speed. |
x1, x2 | The noise element coefficient reflects the degree to which the corresponding noise element causes the input wind speed to deviate from the predicted wind speed. |
P1 | The upper limit power of the wind power affine model |
P2 | The lower limit power of the wind power affine model |
P3 | The upper limit power of an affine optimization model for wind power |
P4 | The lower limit power of an affine optimization model for wind power |
a, b, c, d | Differential coefficient of the affine model after Taylor expansion |
α | The ratio of the number of data in the confidence interval to the number of all data |
ft, it, Ot | Respectively represent the forgetting gate, input gate, and output gate. |
ωf, ωi, ωO | Respectively represent the weight of the forgetting, input, and output gates. |
mt | Represents the input vector of the LSTM model at time t |
ht−1 | Represents the output vector of the hidden layer of the LSTM model at time t − 1 |
RMSE | Root mean square error of wind power prediction |
RMSEerr | The root means a square error of the error data. |
RMS | Correct root means a square error of power data. |
PPI | Predicted power at time i |
PEPi | Error power predicted at time i |
PRPi | Predicted power after correction at time i |
PMi | The actual power at time i |
PEMi | The original error power at time i |
PRMi | The actual original power at time i |
Cap | Total start-up capacity of the wind farm |
xlearn, vlearn | Refer to the position and speed of particle learning rate, respectively |
xunit, vunit | Refer to the position and velocity of the number of implied units of a particle, respectively |
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Model | Description of Model |
---|---|
M1 | The prediction using the traditional LSTM model |
M2 | predicted by the self-moving window LSTM model (namely PSO-SWWLSTM) based on the particle swarm optimization algorithm. |
M3 | The prediction using the PSO-SWWLSTM model, and the error feedback correction model based on the LSTM network (i.e., EFCM) is used to correct the predicted data. |
M4 | The affine optimization model is first used for data correction, and then the PSO-SWLSTM model is used for prediction. |
M5 | The model proposed in this paper. |
Count | Mean | Standard Deviation | Median | Kurtosis | Skewness | Minimum | Maximum | |
---|---|---|---|---|---|---|---|---|
Wind speed (10 m), m/s | 960 | 6.292 | 0.043 | 6.330 | −0.085 | −0.170 | 1.600 | 9.280 |
Wind speed (30 m), m/s | 960 | 7.380 | 0.050 | 7.420 | −0.124 | −0.172 | 1.880 | 10.860 |
Wind speed (50 m), m/s | 960 | 7.948 | 0.054 | 7.990 | −0.141 | −0.173 | 2.020 | 11.690 |
Wind speed (70 m), m/s | 960 | 8.342 | 0.057 | 8.395 | −0.114 | −0.194 | 2.120 | 12.260 |
Temperature, °C | 960 | 27.748 | 0.039 | 27.540 | 32.628 | −3.029 | 14.460 | 30.590 |
Humidity, %rh | 960 | 87.488 | 0.151 | 88.195 | −0.104 | −0.488 | 73.440 | 96.370 |
Pressure, Pa | 960 | 1003.019 | 0.085 | 1003.538 | 1.781 | −1.403 | 994.463 | 1007.409 |
Wind power, MW | 960 | 21.478 | 0.363 | 20.331 | −1.119 | 0.187 | 0 | 44.027 |
Model | RMSE (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Average | |
M1 | 20.57 | 19.32 | 21.35 | 20.03 | 19.56 | 18.87 | 18.69 | 20.22 | 19.67 | 19.31 | 19.759 |
M2 | 17.68 | 16.58 | 14.89 | 15.73 | 16.77 | 14.79 | 15.45 | 17.85 | 16.14 | 14.60 | 16.048 |
M3 | 15.45 | 13.23 | 13.89 | 15.42 | 13.67 | 12.89 | 14.63 | 13.12 | 12.78 | 13.54 | 13.862 |
M4 | 14.78 | 13.76 | 12.47 | 13.89 | 14.68 | 14.25 | 13.69 | 13.57 | 11.48 | 12.59 | 13.516 |
M5 | 10.23 | 7.57 | 8.67 | 7.52 | 12.68 | 10.15 | 7.69 | 8.63 | 9.27 | 8.17 | 9.058 |
Model | MAPE (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Average | |
M1 | 19.35 | 20.65 | 21.35 | 21.80 | 20.87 | 19.18 | 20.22 | 21.49 | 21.58 | 21.48 | 20.797 |
M2 | 19.04 | 19.45 | 19.07 | 19.12 | 18.02 | 18.80 | 18.98 | 18.33 | 19.44 | 19.26 | 18.951 |
M3 | 16.79 | 15.40 | 15.22 | 15.68 | 15.89 | 16.30 | 16.32 | 16.16 | 15.73 | 15.85 | 15.934 |
M4 | 16.69 | 14.08 | 14.46 | 15.88 | 15.03 | 16.19 | 14.47 | 15.71 | 16.76 | 14.17 | 15.344 |
M5 | 12.03 | 13.05 | 13.18 | 11.98 | 11.72 | 14.77 | 14.57 | 13.11 | 13.37 | 11.73 | 12.951 |
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Xiao, Z.; Tang, F.; Wang, M. Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction. Sustainability 2023, 15, 3798. https://doi.org/10.3390/su15043798
Xiao Z, Tang F, Wang M. Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction. Sustainability. 2023; 15(4):3798. https://doi.org/10.3390/su15043798
Chicago/Turabian StyleXiao, Zhengxuan, Fei Tang, and Mengyuan Wang. 2023. "Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction" Sustainability 15, no. 4: 3798. https://doi.org/10.3390/su15043798