Offshore wind power generation and transmission tariff design options

2012 ◽  
Author(s):  
S. Y. Hadush ◽  
R. Belmans
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Design Options

R & D on Offshore Wind Power Generation System in Japan

2009 ◽  
Vol 129 (7) ◽  
pp. 851-854
Author(s):  
Kazuhito Oishi ◽  
Yukinari Fukumoto
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Generation System ◽  
Offshore Wind Power ◽  
Power Generation System

Economic feasibility assessment of cage aquaculture in offshore wind power generation areas in Changhua County, Taiwan

Aquaculture ◽  
2021 ◽  
pp. 737611
Author(s):  
Cheng-Ting Huang ◽  
Farok Afero ◽  
Chun-Wei Hung ◽  
Bo-Ying Chen ◽  
Fan-Hua Nan ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Economic Feasibility ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Feasibility Assessment ◽  
Cage Aquaculture

scholarly journals 1km-GRIDDED WIND SIMULATION OVER ISE BAY FOR FEASIBILITY STUDY OF OFFSHORE WIND POWER GENERATION

2004 ◽  
Vol 12 ◽  
pp. 227-232
Author(s):  
Susumu SHIMADA ◽  
Teruo OHSAWA ◽  
Kazuhito FUKAO ◽  
Atsushi HASHIMOTO ◽  
Tomokazu MURAKAMI ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Feasibility Study ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Ise Bay ◽  
Wind Simulation

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.

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