Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Kumar Shivam, Jong-Chyuan Tzou and Shang-Chen Wu
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Kumar Shivam: Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan
Jong-Chyuan Tzou: Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan
Shang-Chen Wu: Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan

Energies, 2020, vol. 13, issue 7, 1-29

Abstract: Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

Keywords: wind speed forecasting; wind energy; machine learning; convolutional neural network; deep learning architectures; time series; residual networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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