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

Zhu, Qiaomu; Chen, Jinfu; Zhu, Lin; Duan, Xianzhong; Liu, Yilu

doi:10.3390/en11040705

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

Abstract

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.

Authors:

^[1]; Chen, Jinfu ^[1]; Zhu, Lin ^[2]; Duan, Xianzhong ^[1]; Liu, Yilu ^[2]

Huazhong Univ. of Science and Technology, Wuhan (China)
Univ. of Tennessee, Knoxville, TN (United States)

Publication Date:: Wed Mar 21 00:00:00 EDT 2018

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1474549

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Energies

Additional Journal Information:: Journal Volume: 11; Journal Issue: 4; Journal ID: ISSN 1996-1073

Publisher:: MDPI AG

Country of Publication:: United States

Language:: English

Subject:: 17 WIND ENERGY

Citation Formats


                    Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Duan, Xianzhong, and Liu, Yilu. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach.  United States: N. p., 2018. 
Web.  doi:10.3390/en11040705.

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                    Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Duan, Xianzhong, & Liu, Yilu. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach.  United States.  https://doi.org/10.3390/en11040705

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                    Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Duan, Xianzhong, and Liu, Yilu. Wed .  
"Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach".  United States.  https://doi.org/10.3390/en11040705.  https://www.osti.gov/servlets/purl/1474549.

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@article{osti_1474549,

  title        = {Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach},

  author       = {Zhu, Qiaomu and Chen, Jinfu and Zhu, Lin and Duan, Xianzhong and Liu, Yilu},

  abstractNote = {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.},

  doi          = {10.3390/en11040705},

  journal      = {Energies},

  number       = 4,

  volume       = 11,

  place        = {United States},

  year         = {Wed Mar 21 00:00:00 EDT 2018},

  month        = {Wed Mar 21 00:00:00 EDT 2018}

}

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Similar Records

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

Abstract

Citation Formats

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