Chance Constrained Optimization for Wind Power Curve Fitting with Unclean Data

This paper addresses the wind power curve's abnormal detection and regression problems in a unified way. We formulate a chance-constrained optimization problem to obtain an interval neural network (or a set-valued regression model) of the wind power curve. The obtained interval neural network has two properties: (1) the interval locks the area of the normal data, which can be used for abnormal detection; (2) the center of the interval is the fitted wind power curve. We propose a sample-based sigmoidal approximation-based method to solve the formulated chance-constrained optimization problem.