Posters

Abstract

The detection of atypical meteorological data is a critical challenge in the field of environmental monitoring, especially when considering raw data from wind measurement systems. Such data can exhibit several types of anomalies, including sensor errors, wrong behaviors, and offsets, which may arise due to calibration issues, data transmission errors, or external disturbances. Detecting these anomalies is particularly important in the context of wind data, where incorrect readings or misalignments in the time series can lead to significant misinterpretations in wind resource studies. This abstract explores the application of machine learning models, specifically Random Forest (RF) and Gradient Boosting Machines (GBM), to detect and correct atypical behaviors in raw wind data series, focusing on identifying incorrect wind direction readings, time offsets among meteorological stations wind data series, and erroneous measurements. Wind data typically includes key parameters such as horizontal wind speed, vertical wind speed, wind direction, humidity, ambient temperature, pressure, and time-stamped readings from various meteorological stations. However, errors in wind direction and time offsets between different wind series (i.e., discrepancies in timestamp alignment across sensors) are common challenges. Detecting and correcting these anomalies is essential for ensuring the accuracy of subsequent analyses such as wind resource estimation. Traditional statistical methods often fall short in identifying these types of anomalies, especially when dealing with large volumes of raw data. Machine learning, with its ability to identify complex, non-linear patterns, provides an effective solution to this problem. The primary goal of this study is to develop machine learning-based anomaly detection models and preprocessed data actions to identify three main types of atypical behaviors in wind data with the aim of automate tasks: wind direction offsets, time offsets and wrong behaviors such as components breakdowns, icing, wind deviations, etc. Random Forest leverages multiple decision trees to identify features that are most indicative of atypical behavior in wind direction and temporal misalignments. On the other hand, Gradient Boosting Machines (GBM) are used to improve prediction accuracy by iteratively refining the model through boosting weak learners. In the training process, the models are provided with labeled datasets containing normal and anomalous wind data points, these datasets are usually unbalanced series, that means more “non-anomalous” data than “anomalous”, so it’s a challenge itself. The models are trained to recognize patterns that correspond to erroneous measurements or misaligned time series, which may manifest as sudden shifts or inconsistencies, and non-contiguous data points. Feature engineering plays a key role in this process, with key features such as wind speed, direction changes, and time differences between consecutive readings being used to inform model predictions. The performance of the models is evaluated using standard metrics such as precision, recall, F1 score, and accuracy, which provide insight into the models' ability to correctly classify atypical data and minimize false positives. In conclusion, machine learning techniques offer promising solutions for detecting atypical behaviors in wind data series. These models provide a scalable, accurate, and adaptable framework for anomaly detection in large meteorological datasets, enhancing the reliability of wind data analysis.