BOTTOM-UP ESTIMATION AND TOP-DOWN PREDICTION: SOLAR ENERGY PREDICTION COMBINING INFORMATION FROM MULTIPLE SOURCES

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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.
Publisher
INST MATHEMATICAL STATISTICS
Issue Date
2018-12
Language
English
Article Type
Article
Citation

ANNALS OF APPLIED STATISTICS, v.12, no.4, pp.2096 - 2120

ISSN
1932-6157
DOI
10.1214/18-AOAS1145
URI
http://hdl.handle.net/10203/247115
Appears in Collection
MA-Journal Papers(저널논문)
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