Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion

Zhihao Shang, Quan Wen, Yanhua Chen, Bing Zhou and Mingliang Xu
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Zhihao Shang: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Quan Wen: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Yanhua Chen: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Bing Zhou: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Mingliang Xu: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Energies, 2022, vol. 15, issue 8, 1-23

Abstract: As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data.

Keywords: attention mechanism; causal convolutional network; wind speed forecasting; singular spectrum analysis; wind energy (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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