Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast
Abstract
:1. Introduction
2. Materials and Methods
2.1. Prediction Pipeline
2.2. WRF
2.3. Baseline Model
2.4. Artificial Neural Networks
2.5. Data and Training
2.6. Metrics of Model Performance
2.7. Optimization
3. Results and Discussion
4. Conclusions
- The hybrid model combining an ANN for wind speed forecast with an on-site calibrated power curve performs better than the direct model using an ANN with power as its output;
- For the direct model, including a tanh layer to mimic the nonlinear behavior of the power curve improves the forecast accuracy;
- In the ANN inputs, it is preferable to include the NWP results in the past (up to 2 h for a good balance between performance gain and computation cost);
- A proper sample weighting scheme taking into account the skewed power distribution can improve the model performance;
- For a site with complex terrain conditions, the model performance is also heterogeneous. The effectiveness of ANN models in correcting systematic errors of physics-based models is proven.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANN | Artificial Neural Network |
D01 | Domain 1 of Weather Model |
D02 | Domain 2 of Weather Model |
D03 | Domain 3 of Weather Model |
GFS | Global Forecast System |
MAE | Mean Absolute Error |
ME | Maximum Error |
MedAE | Absolute Error |
ML | Machine Learning |
NWP | Numerical Weather Prediction |
P | pressure |
RMSE | Root Mean Squared Error |
SCADA | Supervisory Control And Data Acquisition |
T | temperature |
WD | wind direction |
WRF | Weather Research and Forecasting Model |
WS | wind speed |
WTG | Wind Turbine Generator |
References
- Notton, G.; Nivet, M.L.; Voyant, C.; Paoli, C.; Darras, C.; Motte, F.; Fouilloy, A. Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting. Renew. Sustain. Energy Rev. 2018, 87, 96–105. [Google Scholar] [CrossRef]
- Yousuf, M.; Al-Bahadly, I.; Avci, E. Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting. IEEE Access 2019, 7, 159547–159564. [Google Scholar] [CrossRef]
- Jung, J.; Broadwater, R. Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev. 2014, 31, 762–777. [Google Scholar] [CrossRef]
- Foley, A.; Leahy, P.; Marvuglia, A.; McKeogh, E. Current methods and advances in forecasting of wind power generation. Renew. Energy 2012, 37, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Colak, I.; Sagiroglu, S.; Yesilbudak, M. Data mining and wind power prediction: A literature review. Renew. Energy 2012, 46, 241–247. [Google Scholar] [CrossRef]
- Manero, J.; Béjar, J.; Cortés, U. Wind energy forecasting with neural networks: A literature review. Comput. Sist. 2018, 22, 1085–1098. [Google Scholar] [CrossRef] [Green Version]
- Marugán, A.; Márquez, F.; Perez, J.; Ruiz-Hernández, D. A survey of artificial neural network in wind energy systems. Appl. Energy 2018, 228, 1822–1836. [Google Scholar] [CrossRef] [Green Version]
- Liu, H.; Chen, C.; Lv, X.; Wu, X.; Liu, M. Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Convers. Manag. 2019, 195, 328–345. [Google Scholar] [CrossRef]
- Ramirez-Rosado, I.; Fernandez-Jimenez, L.; Monteiro, C.; Sousa, J.; Bessa, R. Comparison of two new short-term wind-power forecasting systems. Renew. Energy 2009, 34, 1848–1854. [Google Scholar] [CrossRef]
- Vaccaro, A.; Mercogliano, P.; Schiano, P.; Villacci, D. An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting. Electr. Power Syst. Res. 2011, 81, 775–782. [Google Scholar] [CrossRef]
- Zhao, P.; Wang, J.; Xia, J.; Dai, Y.; Sheng, Y.; Yue, J. Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China. Renew. Energy 2012, 43, 234–241. [Google Scholar] [CrossRef]
- Xu, Q.; He, D.; Zhang, N.; Kang, C.; Xia, Q.; Bai, J.; Huang, J. A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Trans. Sustain. Energy 2015, 6, 1283–1291. [Google Scholar] [CrossRef]
- Men, Z.; Yee, E.; Lien, F.S.; Wen, D.; Chen, Y. Short-term wind speed and power forecasting using an ensemble of mixture density neural networks. Renew. Energy 2016, 87, 203–211. [Google Scholar] [CrossRef]
- Mana, M.; Burlando, M.; Meißner, C. Evaluation of two ANN approaches for the wind power forecast in a mountainous site. Int. J. Renew. Energy Res. 2017, 7, 1629–1638. [Google Scholar]
- Mana, M.; Astolfi, D.; Castellani, F.; Meißner, C. Day-ahead wind power forecast through high-resolution mesoscale model: Local computational fluid dynamics versus artificial neural network downscaling. J. Sol. Energy Eng. 2020, 142, 034502. [Google Scholar] [CrossRef]
- Haupt, S.; McCandless, T.; Dettling, S.; Alessandrini, S.; Lee, J.; Linden, S.; Petzke, W.; Brummet, T.; Nguyen, N.; Kosović, B.; et al. Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting. Energies 2020, 13, 1979. [Google Scholar] [CrossRef] [Green Version]
- Hong, T.; Pinson, P.; Fan, S. Global energy forecasting competition 2012. Int. J. Forecast. 2014, 30, 357–363. [Google Scholar] [CrossRef]
- Tabas, D.; Fang, J.; Porté-Agel, F. Wind energy prediction in highly complex terrain by computational fluid dynamics. Energies 2019, 12, 1311. [Google Scholar] [CrossRef] [Green Version]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Yesilbudak, M. Partitional clustering-based outlier detection for power curve optimization of wind turbines. In Proceedings of the 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, UK, 20–23 November 2016; pp. 1080–1084. [Google Scholar] [CrossRef]
- Chollet, F. Keras. 2015. Available online: https://keras.io (accessed on 8 January 2021).
Layer Type | Neurons | Parameters |
---|---|---|
ReLU | 104 | 10,920 |
ReLU | 69 | 7245 |
ReLU | 69 | 4830 |
Dropout | 69 | 0 |
LeakyReLU | 52 | 3640 |
Tanh | 52 | 2756 |
ReLU | 36 | 1908 |
ReLU | 1 | 37 |
Layer Type | Neurons | Parameters |
---|---|---|
ReLu | 80 | 6480 |
ReLu | 53 | 4293 |
ReLu | 53 | 2862 |
Dropout | 53 | 0 |
LeakyReLu | 40 | 2160 |
ReLu | 28 | 1148 |
ReLu | 1 | 29 |
RMSE | MAE | MedAE | ME | |
---|---|---|---|---|
Averaged Error (% kW/kW nominal) | ||||
Test | 13.7 | 9.4 | 6.0 | 89.1 |
Validation | 17.8 | 11.8 | 7.0 | 83.3 |
Baseline | 21.4 | 13.7 | 7.3 | 92.4 |
Hybrid | 16.3 | 10.5 | 5.9 | 81.2 |
Standard Deviation (% kW/kW nominal) | ||||
Test | 1.0 | 0.7 | 0.7 | 6.7 |
Validation | 1.9 | 1.1 | 0.7 | 8.2 |
Baseline | 2.4 | 1.8 | 0.9 | 3.7 |
Hybrid | 1.3 | 0.8 | 0.4 | 6.5 |
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Donadio, L.; Fang, J.; Porté-Agel, F. Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast. Energies 2021, 14, 338. https://doi.org/10.3390/en14020338
Donadio L, Fang J, Porté-Agel F. Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast. Energies. 2021; 14(2):338. https://doi.org/10.3390/en14020338
Chicago/Turabian StyleDonadio, Lorenzo, Jiannong Fang, and Fernando Porté-Agel. 2021. "Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast" Energies 14, no. 2: 338. https://doi.org/10.3390/en14020338