Title: Predicting one-day-ahead wind power capacity factor via functional inverse regression
Authors: Lu-Hung Chen - National Chung Hsing University (Taiwan)
Ci-Ren Jiang - National Taiwan University (Taiwan) [presenting]
Abstract: Inverse regression is an appealing dimension reduction method for regression models with multivariate covariates. Recently, it has been extended to the cases with functional or longitudinal covariates. However, the extensions simply focus on one single functional or longitudinal covariate. Motivated by a real application, we extend functional inverse regression to the cases with multiple functional covariates, whose domains could be different. The asymptotic properties of the proposed estimators are investigated. The computational issues are taken care with data binning, the fast Fourier transformation and random projections on a multi-core computation platform. In addition to simulation studies, the proposed approach is applied to predict the one-day-ahead wind power capacity factors in Germany from 2016 to 2017. Both demonstrate the good performance of our method.