DocumentCode
3583521
Title
Gaussian process prior models for electrical load forecasting
Author
Leith, Douglas J. ; Heidl, Martin ; Ringwood, John V.
fYear
2004
Firstpage
112
Lastpage
117
Abstract
This paper examines models based on Gaussian process (GP) priors for electrical load forecasting. This methodology is seen to encompass a number of popular forecasting methods, such as basic structural models (BSMs) and seasonal auto-regressive intergrated (SARI) as special cases. The GP forecasting models are shown to have some desirable properties and their performance is examined on weekly and yearly Irish load data
Keywords
Gaussian channels; autoregressive processes; load forecasting; Gaussian process; basic structural models; electrical load forecasting; electricity demand; seasonal auto-regressive intergrated; Context modeling; Gaussian processes; Helium; Load forecasting; Load modeling; Network synthesis; Neural networks; Predictive models; Spinning; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Probabilistic Methods Applied to Power Systems, 2004 International Conference on
Print_ISBN
0-9761319-1-9
Type
conf
Filename
1378672
Link To Document