Work

Wind power prediction based on hybrid deep learning and Monte Carlo simulation

Year: 2025

Type: article

Abstract: This study proposes a hybrid deep learning model combining Variational Mode Decomposition (VMD), Convolutional Neural Networks (CNN), and a Three-Dimensional Gated Neural Network (TGNN) to enhance wind power prediction accuracy. VMD decomposes wind power time series into intrinsic mode functions, CNN extracts deep features, and TGNN captures temporal dependencies with error feedback control. A Monte Carlo simulation based on the Central Limit Theorem is introduced to evaluate predictive uncertainty and interval coverage. Experimental results from three wind farms in China demonstrate that the proposed model significantly outperforms baseline hybrid models. Specifically, it achieves a coverage probability (CP) of 0.887, an average interval width (AIW) of 200.870, and a normalized mean square error (NMSE) of 0.057. Compared with the VMD-CNN-LSTM model, the proposed VMD-CNN-TGNN reduces root mean error (RMSE) by approximately 50%, indicating its superior accuracy, robustness, and practical value in wind power forecasting.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2025.112082

Authors: Zhiyong Guo, Qiaoli Han, Fangzheng Wei, Wenkai Qi

Institution: Inner Mongolia Agricultural University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: hybrid

Estimated APC paid: $3,200