Tackling Missing Values in Probabilistic Wind Power Forecasting: A Generative Approach

arXiv:2403.03631v1 Announce Type: new
Abstract: Machine learning techniques have been successfully used in probabilistic wind power forecasting. However, the issue of missing values within datasets due to sensor failure, for instance, has been overlooked for a long time. Although it is natural to consider addressing this issue by imputing missing values before model estimation and forecasting, we suggest treating missing values and forecasting targets indifferently and predicting all unknown values simultaneously based on observations. In this paper, we offer an …

abstract arxiv cs.lg cs.sy datasets eess.sy failure forecasting generative however instance issue machine machine learning machine learning techniques missing values natural power sensor type values wind