Machine Learning and Deterministic Methods for Detection Meteorological Phenomena from Ground Measurements: Application for Low-Level Jet and Sea-Breeze Identification in Northern France

Abstract

This study focused on the detection of mesoscale meteorological phenomena, such as the nocturnal low-level jet (NLLJ) and sea breeze (SB), using automatic deterministic detection wavelet technique algorithms (HWTT and SWT) and the machine learning recurrent neural network (RNN) algorithm. The developed algorithms were applied for detection of NLLJ and SB events from ultrasonic anemometer measurements, performed between January 2018 and December 2019 at a nearshore experimental site in the north of France. Both algorithms identified the SB and NLLJ days successfully. The accuracy of SB event detection by the RNN algorithm attained 95%, and we identified 67 and 78 SB days in 2018 and 2019, respectively. Additionally, a total of 192 and 168 NLLJ days were found in 2018 and 2019, respectively. To demonstrate the capability of the algorithms to detect SB and NLLJ events from near-ground ultrasonic anemometer measurements, analysis of the simultaneous wind lidar measurements available for 86 days were performed. The results show a good agreement between the RNN-based detection method and the lidar observations, detecting 88% of SB. Deterministic algorithms (HWTT and SWT) detected a similar number of NLLJ events and provided high correlation (0.98) with the wind lidar measurements. The meteorological phenomena studied can significantly affect the energy production of offshore wind farms. It was found that the maximum hourly average peak power production could be to 5 times higher than that of the reference day due to higher wind speed observed during NLLJ events. During SB events, hourly average peak power production could be up to 2.5 times higher. In this respect, the developed algorithms applied for analysis, from near-ground anemometer measurements, may be helpful for monitoring and forecasting the meteorological phenomena capable of disturbing the energy production of offshore wind turbines.

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

“…Comparison with ground truth data substantiated the method's efficacy, yielding a precision rate of 93.67% for the dynamic surveillance of offshore wind turbines spanning the Yellow Sea of China and the North Sea of Europe from 2015 to 2021. Furthermore, Roy and colleagues conducted a study in northern France to detect sea breeze and nocturnal low-level jet meteorological events [23]. The researchers developed four distinct algorithms for identifying these events: the Sign Change of Sea-Breeze Component, a recurrent neural network tailored for sea breeze, the Haar wavelet threshold technique for nocturnal low-level jet, and the Symlets wavelet slope technique.…”