A Probabilistic Modeling Strategy for Wind Power and System Demand

2018 ◽  
pp. 625-640
Author(s):  
A. Y. Abdelaziz ◽  
M. M. Othman ◽  
M. Ezzat ◽  
A. M. Mahmoud ◽  
Neeraj Kanwar
Keyword(s):  
Wind Power ◽  
Probabilistic Modeling ◽  
Modeling Strategy

Conditional probabilistic modeling of wind power forecast error

2016 ◽  
Author(s):  
Yida Ye ◽  
Zongxiang Lu ◽  
Ying Qiao ◽  
Xi Ye ◽  
Wei Hu ◽  
...  
Keyword(s):  
Wind Power ◽  
Forecast Error ◽  
Probabilistic Modeling ◽  
Wind Power Forecast

Wind power generation probabilistic modeling using ensemble learning techniques

2020 ◽  
Vol 26 ◽  
pp. 2157-2162
Author(s):  
Rita Banik ◽  
P. Das ◽  
S. Ray ◽  
Ankur Biswas
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Ensemble Learning ◽  
Probabilistic Modeling ◽  
Wind Power Generation ◽  
Learning Techniques

scholarly journals Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaojiao Chen ◽  
Xiuqing Zhang ◽  
Mi Dong ◽  
Liansheng Huang ◽  
Yan Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Short Term Memory ◽  
Offshore Wind ◽  
Power Prediction ◽  
Modeling Strategy ◽  
Power Sequence

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time series of wind power, it is present a two-stage modeling strategy, in which a deep neural network combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. The proposed approach is validated by using the wind power data from an offshore wind farm in China, and the results in comparison with other approaches shows the high prediction preciseness achieved by the proposed approach.

The Wind-Power Automobile

1923 ◽  
Vol 129 (3) ◽  
pp. 171-171
Keyword(s):  
Wind Power

Wind Power Integration: Connection and system operational aspects

2007 ◽  
Author(s):  
Fox ◽  
Jenkins ◽  
O'Malley ◽  
Bryans ◽  
Anaya-Lara ◽  
...  
Keyword(s):  
Wind Power ◽  
Wind Power Integration ◽  
Operational Aspects

Novel Synchronous Generator for Wind Power Generation

2014 ◽  
Vol 2 ◽  
pp. 170-173
Author(s):  
Tsuyoshi Higuchi ◽  
Yuichi Yokoi
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Synchronous Generator ◽  
Wind Power Generation
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