University of Oulu

Wang H, Peng C, Liao B, Cao X, Li S. Wind Power Forecasting Based on WaveNet and Multitask Learning. Sustainability. 2023; 15(14):10816.

Wind power forecasting based on WaveNet and multitask learning

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Author: Wang, Hao1; Peng, Chen2; Liao, Bolin2;
Organizations: 1School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China
2School of Computer Science and Engineering, Jishou University, Jishou 416000, China
3School of Business, Jiangnan University, Wuxi 214122, China
4Faculty of Information Technology and Electrical Engineering, University of Oulu, 90307 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2023
Publish Date: 2023-08-11


Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time series data. First, the maximum information coefficient (MIC) method is applied to handle data features, and the wavelet transform technique is employed to remove noise from the data. Subsequently, WaveNet utilizes its scalable convolutional network to extract representations of wind power data and effectively capture long-range temporal information. These representations are then fed into the MMoE architecture, which treats multistep time series prediction as a set of independent yet interrelated tasks, allowing for information sharing among different tasks to prevent error accumulation and improve prediction accuracy. We conducted predictions for various forecasting horizons and compared the performance of the proposed model against several benchmark models. The experimental results confirm the strong predictive capability of the WaveNet–MMoE framework.

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Series: Sustainability
ISSN: 2071-1050
ISSN-E: 2071-1050
ISSN-L: 2071-1050
Volume: 15
Issue: 14
Article number: 10816
DOI: 10.3390/su151410816
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by the Natural Science Foundation of China under Grant 62006095, by the Natural Science Foundation of Hunan Province, China, under Grant 2021JJ40441, and by the Jishou University Graduate Research and Innovation Project TXJD202303.
Copyright information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (