scholarly journals Are Local Wind Power Resources Well Estimated?

2016 ◽  
pp. 141-144
Keyword(s):  
Wind Power ◽  
Local Wind ◽  
Power Resources

scholarly journals Are local wind power resources well estimated?

2013 ◽  
Vol 8 (1) ◽  
pp. 011005 ◽  
Author(s):  
Erik Lundtang Petersen ◽  
Ib Troen ◽  
Hans E Jørgensen ◽  
Jakob Mann
Keyword(s):  
Wind Power ◽  
Local Wind ◽  
Power Resources

Analysis of Sustainable Development of Inner Mongolia Wind Power

2011 ◽  
Vol 110-116 ◽  
pp. 2421-2425
Author(s):  
Hui Juan Zhai ◽  
Huan Huan Qiao ◽  
Guan Qing Wang
Keyword(s):  
Sustainable Development ◽  
Wind Power ◽  
Inner Mongolia ◽  
Power Transmission ◽  
Wind Power Generation ◽  
Local Wind ◽  
The Sustainable Development ◽  
Resource Conditions ◽  
Wind Resource ◽  
Important Significance

Inner Mongolia region is vast, and developable wind resource accounts for 50%. However, wind power grid has become the local wind development's main bottleneck. Therefore, studying the sustainability of wind power in this region has very important significance. This article from aspects of resource conditions, economic growth, wind power transmission, technical strength and policy environment analyzes the sustainability of Inner Mongolia wind power generation, then draws the conclusion that the bottleneck problem is expected to be solved and the sustainable development is expected to be realized.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Global assessment of onshore wind power resources considering the distance to urban areas

Energy Policy ◽  
2016 ◽  
Vol 91 ◽  
pp. 75-86 ◽  
Author(s):  
Diego Silva Herran ◽  
Hancheng Dai ◽  
Shinichiro Fujimori ◽  
Toshihiko Masui
Keyword(s):  
Wind Power ◽  
Urban Areas ◽  
Global Assessment ◽  
Onshore Wind ◽  
Power Resources

scholarly journals Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting

2011 ◽  
Vol 88 (11) ◽  
pp. 4087-4096 ◽  
Author(s):  
C. Gallego ◽  
P. Pinson ◽  
H. Madsen ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Dynamics ◽  
Local Wind ◽  
Short Term ◽  
Short Term Forecasting
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