A novel deep learning ensemble model with data denoising for short-term wind speed forecasting

2020 ◽  
Vol 207 ◽  
pp. 112524 ◽  
Author(s):  
Zhiyun Peng ◽  
Sui Peng ◽  
Lidan Fu ◽  
Binchun Lu ◽  
Junjie Tang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Ensemble Model ◽  
Short Term ◽  
Wind Speed Forecasting

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Short-term wind speed forecasting system using deep learning for wind turbine applications

2020 ◽  
Vol 10 (6) ◽  
pp. 5779
Author(s):  
Gokhan Erdemir ◽  
Aydin Tarik Zengin ◽  
Tahir Cetin Akinci
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Learning Algorithm ◽  
Integrated System ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Short Term Forecasting

It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.

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