Combined Model for Short-term Wind Power Prediction Based on Deep Neural Network and Long Short-Term Memory. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Xiong, Bangru; Meng, Xinyu; Wang, Ruihan; Wang, Xin; Wang, Zhengxia
• Is Part Of: Journal of physics. Volume 1757:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Short-term wind power prediction -- DNN -- LSTM -- Feature fusion; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1757/1/012095
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Wind power generation is affected by weather and historical wind power, which presents the characteristics of instability and high volatility. Most wind power prediction models ignore physics information. In this paper, a novel combined predicting model that simultaneously considers physics information and historical information is presented to address the drawbacks of existing models. First, the physical characteristics of wind speed, wind direction, and temperature are obtained by Deep Neural Network(DNN), and time-series characteristics from historical wind power are extracted by Long Short-Term Memory(LSTM). Then, the physical features and the time-series features are fully connected for feature fusion to obtain the final time-series physical features. Finally, the short-term wind power prediction is performed according to the obtained merged features. Experimental results demonstrate that the DNN-LSTM model proposed in this paper achieves high accuracy and stability, and provides technical support for wind power system dispatch.