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

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  • Qiaomu Zhu

    (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)

  • Jinfu Chen

    (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)

  • Lin Zhu

    (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

  • Xianzhong Duan

    (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)

  • Yilu Liu

    (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

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.

Suggested Citation

  • Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:705-:d:137311
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    References listed on IDEAS

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    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    3. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis," Renewable Energy, Elsevier, vol. 33(6), pages 1237-1250.
    4. Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
    5. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
    6. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
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