Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting

Wen-Hui Lin, Ping Wang, Kuo-Ming Chao, Hsiao-Chung Lin, Zong-Yu Yang, Yu-Huang Lai

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    Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.
    Original languageEnglish
    Article numbere10335
    JournalApplied Sciences
    Issue number21
    Publication statusPublished - 3 Nov 2021

    Bibliographical note

    Funding Information:
    Funding: This research was funded by the Ministry of Science and Technology of Taiwan under Grant Nos. MOST 110-2410–H-168-003, and Taiwan’s Ministry of Education (MOE) under Grant No. MOE 2000-109CC5-001.

    Funding Information:
    Acknowledgments: This work was jointly supported by Taiwan’s Ministry of Science and Technology under Grant No. MOST 110-2410–H-168-003 and Taiwan’s Ministry of Education (MOE) under Grant No. MOE 2000-109CC5-001.

    Publisher Copyright:
    © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


    • renewable energy
    • wind power forecasting
    • deep learning network
    • temporal convolutional network
    • long short-term memory


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