Wind Power Forecasting Based on WaveNet and Multitask Learning

Hao Wang, Chen Peng (), Bolin Liao, Xinwei Cao and Shuai Li
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Hao Wang: School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China
Chen Peng: School of Computer Science and Engineering, Jishou University, Jishou 416000, China
Bolin Liao: School of Computer Science and Engineering, Jishou University, Jishou 416000, China
Xinwei Cao: School of Business, Jiangnan University, Wuxi 214122, China
Shuai Li: Faculty of Information Technology and Electrical Engineering, University of Oulu, 90307 Oulu, Finland

Sustainability, 2023, vol. 15, issue 14, 1-22

Abstract: 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.

Keywords: wind turbine power forecasting; WaveNet; multitask learning; multigate mixture-of-experts; multistep time series forecasting; maximum information coefficient; wavelet transform (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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