A Novel Hybrid Deep Learning Model for Day-Ahead Wind Power Interval Forecasting

Jianjing Mao, Jian Zhao, Hongtao Zhang and Bo Gu ()
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Jianjing Mao: School of Software, Zhengzhou University of Industrial Technology, Zhengzhou 451150, China
Jian Zhao: State Grid Henan Electric Power Research Institute, Zhengzhou 450003, China
Hongtao Zhang: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Bo Gu: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China

Sustainability, 2025, vol. 17, issue 7, 1-26

Abstract: Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation of power grids. In this paper, we propose a hybrid deep learning model for day-ahead wind power interval forecasting. The model begins by utilizing a Gaussian mixture model (GMM) to cluster daily data with similar distribution patterns. To optimize input features, a feature selection (FS) method is applied to remove irrelevant data. The empirical wavelet transform (EWT) is then employed to decompose both numerical weather prediction (NWP) and wind power data into frequency components, effectively isolating the high-frequency components that capture the inherent randomness and volatility of the data. A convolutional neural network (CNN) is used to extract spatial correlations and meteorological features, while the bidirectional gated recurrent unit (BiGRU) model captures temporal dependencies within the data sequence. To further enhance forecasting accuracy, a multi-head self-attention mechanism (MHSAM) is incorporated to assign greater weight to the most influential elements. This leads to the development of a day-ahead wind power interval forecasting model based on GMM-FS-EWT-CNN-BiGRU-MHSAM. The proposed model is validated through comparison with a benchmark forecasting model and demonstrates superior performance. Furthermore, a comparison with the interval forecasts generated using the NPKDE method shows that the new model achieves higher accuracy.

Keywords: wind power forecast; convolutional neural network; bidirectional gated recurrent unit; Gaussian mixture model; interval forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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