Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2018

Journal or Book Title

The Annals of Applied Statistics

Volume

12

Issue

4

First Page

2096

Last Page

2120

DOI

10.1214/18-AOAS1145

Abstract

Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a new approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention. This approach features a division of the large scale data set into smaller ones with manageable sizes, based on their physical locations, and fit a local model in each area. The local model estimates are then combined sequentially from the specified multilevel models using our novel bottom-up approach for parameter estimation. The prediction, on the other hand, is implemented in a top-down matter. The proposed method is applied to the solar energy prediction problem for the U.S. Department of Energy’s SunShot Initiative.

Comments

This article is published as Hwang, Youngdeok; Lu, Siyuan; Kim, Jae-Kwang. Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources. Ann. Appl. Stat. 12 (2018), no. 4, 2096--2120. doi: 10.1214/18-AOAS1145. Posted with permission.

Copyright Owner

Institute of Mathematical Statistics

Language

en

File Format

application/pdf

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