Wind Data Modeling with Transformed Gaussian Processes
- 2023-07-10 (Mon.), 10:30 AM
- 統計所B1演講廳；茶 會：上午10：10。
- 實體與線上視訊同步進行。
- 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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最後更新日期：2023-07-03 17:04
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