Enhanced Recurrent Neural Network for Short-term Wind Farm Power Output Prediction.
Keywords:
Wind Power Output Prediction, Recurrent Neural Network, Deep learning, oLSTM, ARIMA, MSE
Abstract
Scientists, investors and policy makers have become aware of the importance of providing near accurate spatial estimates of renewable energies. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to weather patterns, which are irregular, especially in climates with erratic weather patterns. This can lead to errors in the predicted potentials. Therefore, recurrent neural networks (RNN) are exploited for enhanced wind-farm power output prediction. A model involving a combination of RNN regularization methods using dropout and long short-term memory (LSTM) is presented. In this model, the regularization scheme modifies and adapts to the stochastic nature of wind and is optimised for the wind farm power output (WFPO) prediction. This algorithm implements a dropout method to suit non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbines wind farm. The model out performs the ARIMA model with up to 80% accuracy.
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References
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[12] D. Lee and R. Baldick, "Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks," IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 501-510, 2014.
[13] H.-z. Li, S. Guo, C.-j. Li, and J.-q. Sun, "A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm," Knowledge-Based Systems, vol. 37, pp. 378-387, 2013/01/01/ 2013.
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[22] B. Kanna and S. N. Singh, "Long term wind power forecast using adaptive wavelet neural network," in 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), 2016, pp. 671-676.
[23] D. T. Mirikitani and N. Nikolaev, "Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling," IEEE Transactions on Neural Networks, vol. 21, no. 2, pp. 262-274, 2010.
[24] J. Brownlee, "How to Use Weight Regularization with LSTM Networks for Time Series Forecasting," p. 1, May 5, 2017 2017.
[25] M. Coto-Jimenez and J. Goddard-Close, "LSTM Deep Neural Networks Postfiltering for Enhancing Synthetic Voices," (in English), International Journal of Pattern Recognition and Artificial Intelligence, Article; Proceedings Paper vol. 32, no. 1, p. 24, Jan 2018, Art. no. 1860008.
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[28] T. Moon, H. Choi, H. Lee, and I. Song, RnnDrop: a novel dropout for RNNs in ASR. 2015, pp. 65-70.
[29] T. Bluche, C. Kermorvant, and J. Louradour, "Where to apply dropout in recurrent neural networks for handwriting recognition?," in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, pp. 681-685.
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[32] P. Society, "phm11 data challenge - condition monitoring of anemometers," https://www.phmsociety.org/competition/pmh/11/problems. , Case study 10/11/2014 2011.
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[34] D. Kececioglu, Reliability engineering handbook. DEStech Publications, Inc, 2002.
[35] R. Maalej and M. Kherallah, "Improving MDLSTM for Offline Arabic Handwriting Recognition Using Dropout at Different Positions," in Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, A. E. P. Villa, P. Masulli, and A. J. Pons Rivero, Eds. Cham: Springer International Publishing, 2016, pp. 431-438.
[36] Y. Fei, J. Hu, K. Gao, J. Tu, W.-q. Li, and W. Wang, "Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models," Journal of critical care, vol. 39, pp. 115-123, 2017.
[37] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
[38] G. Vaca-Castano, S. Das, J. P. Sousa, N. D. Lobo, and M. Shah, "Improved scene identification and object detection on egocentric vision of daily activities," Computer Vision and Image Understanding, vol. 156, pp. 92-103, 3// 2017.
[39] S. V. Ravuri and A. Stolcke, "Recurrent neural network and LSTM models for lexical utterance classification," in INTERSPEECH, 2015, pp. 135-139.
[40] T. N. Sainath et al., "Deep convolutional neural networks for large-scale speech tasks," Neural Networks, vol. 64, pp. 39-48, 2015.
[41] E. Phaisangittisagul, "An Analysis of the Regularization between L2 and Dropout in Single Hidden Layer Neural Network," IEEE Computer society, Conference Paper vol. DOI 10.1109/ISMS.2016.14, no. 2166-0670/16, p. 6, 2016 2016.
Published
2019-02-11
How to Cite
Eze, E. C., & Chatwin, C. R. (2019). Enhanced Recurrent Neural Network for Short-term Wind Farm Power Output Prediction. IJRDO-Journal of Applied Science, 5(2), 28-35. https://doi.org/10.53555/as.v5i2.2664
Section
Articles
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