气候变化领域集成服务门户(CCPortal): A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition-Convolutional Neural Network-Three-Dimensional Gated Neural Network

CCPortal

DOI	10.3390/su16083474
	A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition-Convolutional Neural Network-Three-Dimensional Gated Neural Network
	Guo, Zhiyong; Wei, Fangzheng; Qi, Wenkai; Han, Qiaoli; Liu, Huiyuan; Feng, Xiaomei; Zhang, Minghui
发表日期	2024
EISSN	2071-1050
起始页码	16
结束页码	8
卷号	16 期号:8
英文摘要	In response to the global challenge of climate change and the shift away from fossil fuels, the accurate prediction of wind power generation is crucial for optimizing grid operations and managing energy storage. This study introduces a novel approach by integrating the proportional-integral-derivative (PID) control theory into wind power forecasting, employing a three-dimensional gated neural (TGN) unit designed to enhance error feedback mechanisms. The proposed empirical mode decomposition (EMD)-convolutional neural network (CNN)-three-dimensional gated neural network (TGNN) framework starts with the pre-processing of wind data using EMD, followed by feature extraction via a CNN, and time series forecasting using the TGN unit. This setup leverages proportional, integral, and differential control within its architecture to improve adaptability and response to dynamic wind patterns. The experimental results show significant improvements in forecasting accuracy; the EMD-CNN-TGNN model outperforms both traditional models like autoregressive integrated moving average (ARIMA) and support vector regression (SVR), and similar neural network approaches, such as EMD-CNN-GRU and EMD-CNN-LSTM, across several metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). These advancements substantiate the model's effectiveness in enhancing the precision of wind power predictions, offering substantial implications for future renewable energy management and storage solutions.
英文关键词	neural networks; wind power; time series prediction; PID
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:001220345900001
来源期刊	SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297526
作者单位	Inner Mongolia Agricultural University; Inner Mongolia Agricultural University
推荐引用方式 GB/T 7714	Guo, Zhiyong,Wei, Fangzheng,Qi, Wenkai,et al. A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition-Convolutional Neural Network-Three-Dimensional Gated Neural Network[J],2024,16(8).
APA	Guo, Zhiyong.,Wei, Fangzheng.,Qi, Wenkai.,Han, Qiaoli.,Liu, Huiyuan.,...&Zhang, Minghui.(2024).A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition-Convolutional Neural Network-Three-Dimensional Gated Neural Network.SUSTAINABILITY,16(8).
MLA	Guo, Zhiyong,et al."A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition-Convolutional Neural Network-Three-Dimensional Gated Neural Network".SUSTAINABILITY 16.8(2024).