A Robust Hybrid Machine Learning-Based Modeling Technique for Wind Power Production Estimates

The need to accurately estimate wind power is essential to the design and deployment of individual wind turbines and wind farms. The estimation problem is framed as wind power curve modeling. Lately, machine learning techniques have been used to model power curves and provide power estimates. Such models rely on the fact that all outliers are removed from the raw wind data before they are used in modeling and estimation since outliers can adversely affect performance. However, generating outlier-free data is not always possible. Robust models and robust objective functions can be two effective ways to obtain accurate power curves in the presence of outliers. In this paper, a robust density-based clustering technique (DBSCAN) to first identify outliers in the dataset is proposed, followed by artificial neural network (ANN) models that are trained using the outlier-free data to obtain accurate power curve estimates. ANNs are trained using a range of optimization methods and are compared in this study. Preliminary results show the proposed method is superior to probabilistic models that use error-functions to generate accurate power curves and that the proposed hybrid model can generate more accurate power output estimations in the presence of outliers compared to deterministic models such as integrated curve fitting models that are known to be robust.