A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
Abstract
:1. Introduction
- (a)
- According to the characteristics of wind power forecasting, a deep learning framework DWT_AE_BiLSTM is first proposed.
- (b)
- Through the discrete wavelet transform technology, the nonstationary original data is decomposed into several subsequences, and the original data is filtered and denoised.
- (c)
- An autoencoder is employed to extract highly nonlinear feature data, and then the extracted hidden feature data is input into the BiLSTM framework to predict power generation.
2. Preliminaries
2.1. Discrete Wavelet Transform
2.2. Autoencoder
2.3. Bidirectional LSTM
3. Algorithm Framework
- (a)
- Data processing and denoising module: Firstly, the missing data on a wind farm is interpolated and corrected. Then, the discrete wavelet transform technology is used to decompose the nonstationary wind power time series data into low-frequency component and high-frequency component. These components exhibit a greater degree of stationarity and may be forecasted more easily. Input data mainly includes wind tower observation data, wind farm total active power and numerical weather prediction (NWP) data. Wind tower observation data and NWP data include 12 meteorological elements, i.e., wind speed and direction at heights of 10 m, 30 m, 50 m and 70 m, and turbine hub, temperature, humidity and pressure. All of the data time resolutions are 15 min.
- (b)
- Feature extract module: Based on step (a), in addition to the actual power of the power station, there are 13 elements in total. Each element takes five elements in chronological order to form a 65-dimensional vector, which is input into the autoencoder. The features are compressed into a 30-dimensional vector through the training of the autoencoder.
- (c)
- Forecast module: The compressed features in step (b) are input into the bidirectional LSTM to predict the short-term power generation of the wind farm combined with NWP. Bidirectional, two-layer stacked LSTMs are used. We apply the Adam optimization method for training. The grid search method is used to determine the hyper-parameters, and the optimal configuration of model parameters is obtained from the validation set. The final optimal parameters learn rate = 1 × 10−3 and batch size = 128. The dataset utilized in this study comprises data from the calendar year 2018, which has been divided into training and validation sets consisting of 70% and 30% of the data, respectively. In order to evaluate the predictive performance of the model, data from four representative months of the year 2019 were handpicked for comparison against the forecasted outcomes. The results are illustrated in Figure 3, which displays the respective losses of the training and validation sets for Wind Farm #1.
4. Experimental Design
4.1. Data Description
4.2. Performance Evaluation Metrics
4.3. Results and Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Month | Algorithms | PA (%) | MAE (MW) | MAPE (%) |
---|---|---|---|---|
1 | DWT_AE_BiLSTM | 84.69 | 12.03 | 10.94 |
AE_BiLSTM | 83.40 | 13.10 | 11.91 | |
LSTM | 82.08 | 14.25 | 12.95 | |
BP | 78.24 | 17.48 | 15.89 | |
4 | DWT_AE_BiLSTM | 82.36 | 13.58 | 12.35 |
AE_BiLSTM | 81.33 | 15.73 | 14.30 | |
LSTM | 79.04 | 16.33 | 14.85 | |
BP | 78.77 | 16.93 | 15.39 | |
7 | DWT_AE_BiLSTM | 83.63 | 11.65 | 12.41 |
AE_BiLSTM | 82.68 | 12.39 | 11.26 | |
LSTM | 82.10 | 12.57 | 11.43 | |
BP | 81.93 | 12.60 | 11.45 | |
10 | DWT_AE_BiLSTM | 90.69 | 6.34 | 5.76 |
AE_BiLSTM | 89.76 | 7.14 | 6.49 | |
LSTM | 88.30 | 9.05 | 8.23 | |
BP | 86.99 | 10.02 | 9.11 |
Month | Algorithms | PA(%) | MAE(MW) | MAPE(%) |
---|---|---|---|---|
1 | DWT_AE_BiLSTM | 84.75 | 29.27 | 13.30 |
AE_BiLSTM | 81.42 | 30.41 | 13.82 | |
LSTM | 80.30 | 31.51 | 14.32 | |
BP | 79.45 | 33.19 | 15.09 | |
4 | DWT_AE_BiLSTM | 82.65 | 30.31 | 13.78 |
AE_BiLSTM | 81.71 | 33.94 | 15.43 | |
LSTM | 80.45 | 34.23 | 15.56 | |
BP | 80.35 | 34.47 | 15.67 | |
7 | DWT_AE_BiLSTM | 84.11 | 28.17 | 12.80 |
AE_BiLSTM | 83.65 | 29.36 | 13.35 | |
LSTM | 83.23 | 30.38 | 13.81 | |
BP | 82.95 | 32.42 | 14.74 | |
10 | DWT_AE_BiLSTM | 84.35 | 28.18 | 12.81 |
AE_BiLSTM | 81.57 | 29.49 | 13.40 | |
LSTM | 81.31 | 32.07 | 14.58 | |
BP | 80.25 | 34.94 | 15.88 |
Month | Algorithms | PA(%) | MAE(MW) | MAPE(%) |
---|---|---|---|---|
1 | DWT_AE_BiLSTM | 82.23 | 15.17 | 12.01 |
AE_BiLSTM | 81.47 | 15.53 | 12.30 | |
LSTM | 81.02 | 18.81 | 14.89 | |
BP | 79.65 | 19.58 | 15.50 | |
4 | DWT_AE_BiLSTM | 82.12 | 16.86 | 13.35 |
AE_BiLSTM | 81.59 | 17.10 | 13.54 | |
LSTM | 81.10 | 19.38 | 15.34 | |
BP | 79.42 | 20.65 | 16.35 | |
7 | DWT_AE_BiLSTM | 82.00 | 16.37 | 12.96 |
AE_BiLSTM | 81.48 | 17.30 | 13.70 | |
LSTM | 79.47 | 18.91 | 14.97 | |
BP | 78.45 | 21.25 | 16.83 | |
10 | DWT_AE_BiLSTM | 88.66 | 8.27 | 6.55 |
AE_BiLSTM | 87.65 | 10.07 | 7.97 | |
LSTM | 84.12 | 14.03 | 11.11 | |
BP | 83.73 | 14.89 | 11.79 |
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Xu, P.; Zhang, M.; Chen, Z.; Wang, B.; Cheng, C.; Liu, R. A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction. Appl. Sci. 2023, 13, 4042. https://doi.org/10.3390/app13064042
Xu P, Zhang M, Chen Z, Wang B, Cheng C, Liu R. A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction. Applied Sciences. 2023; 13(6):4042. https://doi.org/10.3390/app13064042
Chicago/Turabian StyleXu, Peihua, Maoyuan Zhang, Zhenhong Chen, Biqiang Wang, Chi Cheng, and Renfeng Liu. 2023. "A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction" Applied Sciences 13, no. 6: 4042. https://doi.org/10.3390/app13064042
APA StyleXu, P., Zhang, M., Chen, Z., Wang, B., Cheng, C., & Liu, R. (2023). A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction. Applied Sciences, 13(6), 4042. https://doi.org/10.3390/app13064042