Wind Data Modeling with Transformed Gaussian Processes
- 2023-07-10 (Mon.), 10:30 AM
- Auditorium, B1F, Institute of Statistical Science；The tea reception will be held at 10:10.
- Online live streaming through Cisco Webex will be available.
- Dr. Jaehong Jeong
- Department of Mathematics and the Department of Applied Statistics, Hanyang University
Abstract
Wind energy has substantial potential for future energy portfolios without negatively impacting the environment. In developing national and worldwide energy plans, understanding the spatio-temporal pattern of wind is crucial. We propose a statistical model that aims at reproducing the data-generating mechanism of climate ensembles for global monthly wind data. Inferences based on a multi-step conditional likelihood approach are achieved by balancing memory storage and distributed computation for a large data set. Additionally, we discuss a general strategy for modeling non-Gaussian stochastic processes by transforming underlying Gaussian processes.
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Update：2023-07-10 16:52
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