Wind power forecasting – A data-driven method along with gated recurrent neural network

Abstract Effective wind power prediction will facilitate the world’s long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.

[1]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Shuaishuai Lin,et al.  Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China , 2018, Journal of Cleaner Production.

[3]  Luisa Pagnini,et al.  Experimental power curve of small-size wind turbines in turbulent urban environment , 2015 .

[4]  Ke Wang,et al.  A novel deep learning ensemble model with data denoising for short-term wind speed forecasting , 2020 .

[5]  Zhifeng Sun,et al.  Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection , 2019, Energies.

[6]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[9]  Stephen Marshall,et al.  Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.

[11]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .

[12]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[13]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[14]  Jaehoon Jung,et al.  Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea , 2020, Journal of Cleaner Production.

[15]  Ali Lahouar,et al.  Hour-ahead wind power forecast based on random forests , 2017 .

[16]  Fausto Pedro García Márquez,et al.  Optimal Maintenance Management of Offshore Wind Farms , 2016 .

[17]  Tim Rubert,et al.  Lifetime extension of onshore wind turbines: A review covering Germany, Spain, Denmark, and the UK , 2018 .

[18]  Zi Lin,et al.  A Critical Review of Wind Power Forecasting Methods—Past, Present and Future , 2020, Energies.

[19]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[20]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[21]  Ali Mohammad Ranjbar,et al.  Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine , 2010 .

[22]  Lin Lu,et al.  Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model , 2016 .

[23]  Xiaolei Liu,et al.  Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network , 2020, Energy.

[24]  Warren B. Powell,et al.  Optimal Energy Commitments with Storage and Intermittent Supply , 2011, Oper. Res..

[25]  Chun Man Chan,et al.  Blade Shape Optimization of the Savonius Wind Turbine Using a Genetic Algorithm , 2018 .

[26]  Jiankun Liu,et al.  Aerodynamic optimization for variable-speed wind turbines based on wind energy capture efficiency , 2018, Applied Energy.

[27]  Benjamin Lev,et al.  Optimal decision-making via binary decision diagrams for investments under a risky environment , 2017, Int. J. Prod. Res..

[28]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[29]  Goran Nenadic,et al.  Machine learning methods for wind turbine condition monitoring: A review , 2019, Renewable Energy.

[30]  Viviana Cocco Mariani,et al.  Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network , 2020, Energy Conversion and Management.

[31]  I. V. D. Hoven.  POWER SPECTRUM OF HORIZONTAL WIND SPEED IN THE FREQUENCY RANGE FROM 0.0007 TO 900 CYCLES PER HOUR , 1957 .

[32]  Xu Chen,et al.  Improvement of reliability and wind power generation based on wind turbine real-time condition assessment , 2019 .

[33]  Xiaolei Liu,et al.  Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks , 2020, International Journal of Electrical Power & Energy Systems.

[34]  Zhong‐qiang Wu,et al.  Maximum wind power tracking based on cloud RBF neural network , 2016 .

[35]  B. Lev,et al.  New Pipe Notch Detection and Location Method for Short Distances employing Ultrasonic Guided Waves , 2017 .

[36]  Jesús M. Torres,et al.  Using Deep Learning to Predict Sentiments: Case Study in Tourism , 2018, Complex..

[37]  C. Furlanello,et al.  Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .

[38]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[39]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[40]  Xiaolei Liu,et al.  Prediction of two-phase flow patterns in upward inclined pipes via deep learning , 2020, Energy.

[41]  Tim Stratford,et al.  Code Comparison of a NREL-FAST Model of the Levenmouth Wind Turbine With the GH Bladed Commissioning Results , 2018, Volume 10: Ocean Renewable Energy.

[42]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[43]  Khaled M. Abo-Al-Ez,et al.  A hybrid neuro-fuzzy power prediction system for wind energy generation , 2016 .

[44]  Mayorkinos Papaelias,et al.  Wind turbine reliability analysis , 2013 .

[45]  Farshid Keynia,et al.  A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets , 2020, Energy Conversion and Management.