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Open AccessFeature PaperArticle

Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

1
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Energies 2018, 11(4), 705; https://doi.org/10.3390/en11040705
Received: 11 February 2018 / Revised: 4 March 2018 / Accepted: 20 March 2018 / Published: 21 March 2018
(This article belongs to the Special Issue Sustainable and Renewable Energy Systems)
Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc. View Full-Text
Keywords: convolutional neural networks; deep learning; machine learning; spatio-temporal correlation; wind speed prediction convolutional neural networks; deep learning; machine learning; spatio-temporal correlation; wind speed prediction
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Zhu, Q.; Chen, J.; Zhu, L.; Duan, X.; Liu, Y. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach. Energies 2018, 11, 705.

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