Using Unsupervised Machine Learning for Outlier Detection in Data to Improve Wind Power Production Prediction
Abstract: The expansion of wind power for electrical energy production has increased in recent years and shows no signs of slowing down. This unpredictable source of energy has contributed to destabilization of the electrical grid causing the energy market prices to vary significantly on a daily basis. For energy producers and consumers to make good investments, methods have been developed to make predictions of wind power production. These methods are often based on machine learning were historical weather prognosis and wind power production data is used. However, the data often contain outliers, causing the machine learning methods to create inaccurate predictions. The goal of this Master’s Thesis was to identify and remove these outliers from the data so that the accuracy of machine learning predictions can improve. To do this an outlier detection method using unsupervised clustering has been developed and research has been made on the subject of using machine learning for outlier detection and wind power production prediction.
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