scholarly journals High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

2021 ◽  
Author(s):  
Louis de Montera ◽  
Henrick Berger ◽  
Romain Husson ◽  
Pascal Appelghem ◽  
Laurent Guerlou ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Wind Speed ◽  
Numerical Model ◽  
Wind Power ◽  
Learning Algorithm ◽  
Offshore Wind ◽  
Machine Learning Algorithm ◽  
Power Maps ◽  
Sar Data

Abstract. This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

Satellite Observations for Better Characterization of Sea Surface Wind Field and Offshore Wind Energy Resource Assessment

2021 ◽  
Author(s):  
Mauricio Fragoso ◽  
Louis De Montera ◽  
Romain Husson ◽  
Henrick Berger ◽  
Pascal Appelghem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Numerical Model ◽  
Wind Power ◽  
Wind Farm ◽  
Learning Algorithm ◽  
Surface Wind ◽  
Energy Resource ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Sar Data

Abstract This paper presents a method to generate maps of offshore wind power at turbine hub height from spaceborne Synthetic Aperture Radar (SAR) data. Two techniques based on machine learning are presented. The first can be trained with metocean buoys and the second one, more precise, requires on-site profiling Lidars. If Lidars are not available, SAR surface winds at 10m are improved with machine learning. They are then extrapolated at 40m with a classical power law, and then at higher altitudes with an atmospheric numerical model. If profiling Lidars are available, parameters from the numerical model are added as input to the machine learning algorithm and the training is performed directly at turbine hub height with the Lidar data. Once the wind at turbine hub height is obtained, the wind power is then calculated using a Weibull distribution. The resulting maps are compared with the outputs of the numerical model. The maps based on SAR data provide a much higher level of detail and a better estimation of the coastal gradient, which is important to optimize wind farm siting and estimate the potential energy production. The accuracy of the wind power is found to be in the range ±5% compared to the Lidars.

scholarly journals Crop Classification by Machine Learning Algorithm Using C-band SAR Data

2017 ◽  
Vol 56 (4) ◽  
pp. 143-148
Author(s):  
Yuki YAMAYA ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Crop Classification ◽  
Sar Data

scholarly journals Crop Classification by Machine Learning Algorithm Combined X-band and C-band SAR Data

2020 ◽  
Vol 59 (6) ◽  
pp. 259-274
Author(s):  
Yuki YAMAYA ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
Kan-ichiro MOCHIZUKI ◽  
Xiufeng WANG ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
X Band ◽  
Crop Classification ◽  
Sar Data

scholarly journals Crop Classification by Machine Learning Algorithm with Combination of X- and C-band SAR Data

2018 ◽  
Vol 57 (2) ◽  
pp. 78-83
Author(s):  
Yuki YAMAYA ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Crop Classification ◽  
Sar Data

scholarly journals Wind Resource Assessment using Machine Learning Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1062-1066
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Tamil Nadu ◽  
Learning Algorithm ◽  
Resource Assessment ◽  
Machine Learning Algorithm ◽  
Wind Speed Prediction ◽  
Wind Resource Assessment ◽  
Speed Prediction ◽  
Wind Resource

The wind speed prediction is very important for wind resource assessment, renewable energy integration in to the electricity grid, electricity marketing and so on. Because of the arbitrary fluctuation characteristics of wind, the prediction results may change quickly. This enhances the significance of the accurate wind speed prediction The objective of this paper is to predict the wind speed for Tamil Nadu cities using machine learning algorithm. There are three broad categories of wind forecasting models namely physical model, statistical and computational models and hybrid models. Artificial Neural Network is the most commonly used method for wind speed prediction. Recently machine learning and deep learning algorithms are widely used for forecasting applications. In this work wind speed is predicted for Tamil Nadu cities using decision tree regression algorithm. The Machine Learning (ML) model is trained using measured wind speed data for six cities of India collected from India Meteorological Department (IMD), Pune. The ML model based on decision tree regression algorithm is good in prediction with better performance metrics of MSE in the range of 0.3 to 1.2 m/s and R2 =0.87.

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