Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Huu Khoa Minh Nguyen, Quoc-Dung Phan, Yuan-Kang Wu () and Quoc-Thang Phan
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Huu Khoa Minh Nguyen: Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 70000, Vietnam
Quoc-Dung Phan: Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 70000, Vietnam
Yuan-Kang Wu: Department of Electrical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
Quoc-Thang Phan: Department of Electrical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan

Energies, 2023, vol. 16, issue 9, 1-20

Abstract: Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

Keywords: wind power forecasting; multi-step prediction; similar time series; Stacked Temporal Convolutional Network (S-TCN) (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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