LCE  Vol.12 No.1 , March 2021
Wind Energy Prediction Using Machine Learning
Abstract: Wind energy prediction represents an important and active field in the renewable energy sector. Since renewable energy sources are integrated into existing grids and combined with traditional sources, knowing the amount of energy that will be produced is key in minimizing the operational cost of the wind farm and safe operation of the power grid. In this context, we propose a comparative and comprehensive study of artificial neural networks, support vector regression, random trees, and random forest, and present the pros and cons of implementing the aforementioned techniques. A step-by-step approach based on the CRISP-DM data mining framework reveals the thought process end-to-end, including feature engineering, metrics selection, model selection, or hyperparameter tuning. Using the selected metrics for model evaluation, we provide a summary highlighting the optimal results and the trade-off between performance and the resources expended to achieve these results. This research is also intended to provide guidance for wind energy professionals, filling the gap between purely academic research and real-world business use cases, providing the exact architectures and selected hyperparameters.
Cite this paper: Buturache, A. and Stancu, S. (2021) Wind Energy Prediction Using Machine Learning. Low Carbon Economy, 12, 1-21. doi: 10.4236/lce.2021.121001.

[1]   Barbounis, T. G., Theocharis, J. B., Alexiadis, M. C., & Dokopoulos, P. S. (2006). Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models. IEEE Transactions on Energy Conversion, 21, 273-284.

[2]   Basak, D., Pal, S., Ch, D., & Patranabis, R. (2007). Support Vector Regression. Neural Information Processing—Letters and Reviews, 11, 203-224.

[3]   Ben Ishak, A. (2016). Variable Selection Using Support Vector Regression and Random Forests: A Comparative Study. Intelligent Data Analysis, 20, 83-104.

[4]   Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32.

[5]   Catalao, J. P. S., Pousinho, H. M. I., & Mendes, V. M. F. (2011). Short-Term Wind Power Forecasting in Portugal by Neural Networks and Wavelet Transform. Renewable Energy, 36, 1245-1251.

[6]   Damousis, I. G., & Dokopoulos, P. (2001). A Fuzzy Expert System for the Forecasting of Wind Speed and Power Generation in Wind Farms. PICA 2001. Innovative Computing for Power—Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications, Sydney, 20-24 May 2001, 63-69.

[7]   De Giorgi, M. G., Campilongo, S., Ficarella, A., & Congedo, P. (2014). Comparison between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Energies, 7, 5251-5272.

[8]   Engel, H., Hensley, R., Knupfer, S., & Sahdev, S. (2018). The Potential Impact of Electric Vehicles on Global Energy Systems.

[9]   European Commission (2013). Renewable Energy Directive: Cooperation Mechanisms.

[10]   European Commission (2020). Climate Strategies and Targets: 2030 Climate & Energy Framework.

[11]   European Parliament (2018a). Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy from Renewable Sources.

[12]   European Parliament (2018b). Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency.

[13]   Eurostat (2020). Renewable Energy Statistics.

[14]   Evans, R., & Gao, J. (2016). DeepMind AI Reduces Google Data Centre Cooling Bill by 40%.

[15]   Fawcett, T., & Provost, F. (2013). Data Science for Business. Newton, MA: O’Relly Media.

[16]   Flores, P., Tapia, A., & Tapia, G. (2005). Application of a Control Algorithm for Wind Speed Prediction and Active Power Generation. Renewable Energy, 30, 523-536.

[17]   Focken, U., Lange, M., & Waldl, H.-P. (2001). Previento—A Wind Power Prediction System with an Innovative Upscaling Algorithm. In European Wind Energy Conference & Exhibition (pp. 826-829). Copenhagen: European Wind Energy Association.

[18]   Gani, W., Taleb, H., & Limam, M. (2010). Support Vector Regression Based Residual Control Charts. Journal of Applied Statistics, 37, 309-324.

[19]   Gao, G., Li, J., & Wen, Y. (2019). Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning. arXiv:1901.04693.

[20]   Glorot, X., & Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feed forward Neural Networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (pp. 249-256). Sardinia: ML Research Press.

[21]   He, D., & Liu, R. (2012). Ultra-Short-Term Wind Power Prediction Using ANN Ensemble Based on PCA. In Proceedings of the 7th International Power Electronics and Motion Control Conference (pp. 2108-2112). Harbin: IEEE.

[22]   Hinton, G., Srivastava, N., & Swersky, K. (2020). Lecture 6a: Overview of Mini-Batch Gradient Descent.

[23]   Hunter, D. K., Yu, H., Pukish, M. S., Kolbusz, J., & Wilamowski, B. M. (2012). Selection of Proper Neural Network Sizes and Architectures—A Comparative Study. IEEE Transactions on Industrial Informatics, 8, 228-240.

[24]   International Energy Agency (2018). World Energy Outlook 2018.

[25]   Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial Neural Networks: A Tutorial. Computer, 29, 31-44.

[26]   Kariniotakis, G., Stavrakakis, G., & Nogaret, E. F. (1996). Wind Power Forecasting Using Advanced Neural Networks Models. IEEE Transactions on Energy Conversion, 11, 762-767.

[27]   Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations.

[28]   Kolhe, M., Lin, T. C., & Maunuksela, J. (2011). GA-ANN for Short-Term Wind Energy Prediction. Asia-Pacific Power and Energy Engineering Conference, Wuhan, 25-28 March 2011, 1-6.

[29]   Kotsiantis, S. (2011). Combining Bagging, Boosting, Rotation Forest and Random Subspace Methods. Artificial Intelligence Review, 35, 223-240.

[30]   Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems.

[31]   Kusiak, A., & Xu, G. L. (2012). Modeling and Optimization of HVAC Systems Using Dynamic Neural Network. Energy, 42, 241-250.

[32]   Lahouar, A., & Ben Hadj Slama, J. (2017). Hour-Ahead Wind Power Forecast Based on Random Forests. Renewable Energy, 109, 529-541.

[33]   Landberg, L., & Watson, S. J. (1994). Short-Term Prediction of Local Wind Conditions. Boundary-Layer Meteorology, 70, 171-195.

[34]   LeCun, Y., Bottou, L., Orr, G. B., & Müller, K.-R. (2000). Efficient BackProp.

[35]   Liu, Y., Sun, Y., Infield, D., Zhao, Y., Han, S., & Yan, J. (2016). A Hybrid Forecasting Method for Wind Power Ramp Based on Orthogonal Test and Support Vector Machine (OT-SVM). IEEE Transactions on Sustainable Energy, 8, 451-457.

[36]   Lydia, A. A., & Francis, F. S. (2019). Adagrad—An Optimizer for Stochastic Gradient Descent. International Journal of Information and Computing Science, 6, 566-568.

[37]   Madhiarasan, M., & Deepa, S. N. (2016). Comparative Analysis on Hidden Neurons Estimation in Multi Layer Perceptron Neural Networks for Wind Speed Forecasting. Artificial Intelligence Review, 48, 449-471.

[38]   Marsh, J. (2019). Five Types of Renewable Energy Sources: Best Alternatives to Fossil Fuels.

[39]   Niayifar, A., & Porté-Agel, F. (2015). A New Analytical Model for Wind Farm Power Prediction. Journal of Physics: Conference Series, 625, 012039.

[40]   Open Power System Data (2020). Open Power System Data: A Free and Open Data Platform for Power System Modelling.

[41]   Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D. (2011). Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers. International Journal of Computer Theory and Engineering, 3, 332-337.

[42]   Perez, M. (2002). Wind Forecasting Activities. In Proceedings of the First IEA Joint Action Symposium (pp. 197-214). Norrkoping: FOI-Swedish Defence Research Agency.

[43]   Prada, J., & Dorronsoro, J. R. (2015). SVRs and Uncertainty Estimates in Wind Energy Prediction. In I. Rojas, G. Joya, & A. Catala (Eds.), International Work-Conference on Artificial Neural Networks 2015: Advances in Computational Intelligence (pp. 564-577). Palma de Mallorca: Springer.

[44]   Ritchie, H., & Roser, M. (2017). Fossil Fuels.

[45]   Shetty, B. (2019). Curse of Dimensionality.

[46]   Singh, V. (2016). Application of Artificial Neural Networks for Predicting Generated Wind Power. International Journal of Advanced Computer Science and Applications, 7, 250-253.

[47]   Smola, A. J., & Schölkopf, B. (1998). On a Kernel-Based Method for Pattern Recognition, Regression, Approximation and Operator Inversion. Algorithmica, 22, 211-231.

[48]   Smola, A. J., & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199-222.

[49]   Steorts, R. C. (2020). Tree Based Methods: Regression Trees.

[50]   Taylor, J. (2020). Regression Trees.

[51]   Torres-Barrán, A., Alonso, á., & Dorronsoro, J. R. (2017). Regression Tree Ensembles for Wind Energy and Solar Radiation Prediction. Neurocomputing, 326-327, 151-160.

[52]   U.S. Department of Energy (2011). 2011 Buildings Energy Data Book.

[53]   Wang, H., Sun, J., Sun, J., & Wang, J. (2017). Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models. Energies, 10, 1522.

[54]   Wei, T., Wang, Y., & Zhu, Q. (2017). Deep Reinforcement Learning for Building HVAC Control. In Proceedings of the 54th Annual Design Automation Conference (Article No. 22). New York: Association for Computing Machinery.

[55]   Willuhn, M. (2019). Renewable Energy Investment to Increase by $210 Billion over Five Years.

[56]   Zhu, Z., Zhou, D., & Fan, Z. (2016). Short Term Forecast of Wind Power Generation Based on SVM with Pattern Matching. In 2016 IEEE International Energy Conference (pp. 1-6). Leuven: IEEE.