期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
卷 64, 期 1, 页码 139-155出版社
WILEY
DOI: 10.1111/rssc.12068
关键词
Correlated additive model; Electricity load prediction; Generalized additive model estimation
资金
- Engineering and Physical Sciences Research Council [EP/K005251/1]
- EPSRC [EP/K005251/2, EP/K005251/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K005251/2] Funding Source: researchfish
We consider an application in electricity grid load prediction, where generalized additive models are appropriate, but where the data set's size can make their use practically intractable with existing methods. We therefore develop practical generalized additive model fitting methods for large data sets in the case in which the smooth terms in the model are represented by using penalized regression splines. The methods use iterative update schemes to obtain factors of the model matrix while requiring only subblocks of the model matrix to be computed at any one time. We show that efficient smoothing parameter estimation can be carried out in a well-justified manner. The grid load prediction problem requires updates of the model fit, as new data become available, and some means for dealing with residual auto-correlation in grid load. Methods are provided for these problems and parallel implementation is covered. The methods allow estimation of generalized additive models for large data sets by using modest computer hardware, and the grid load prediction problem illustrates the utility of reduced rank spline smoothing methods for dealing with complex modelling problems.
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