Title :
Gaussian process prior models for electrical load forecasting
Author :
Leith, Douglas J. ; Heidl, Martin ; Ringwood, John V.
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;
Conference_Titel :
Probabilistic Methods Applied to Power Systems, 2004 International Conference on
Print_ISBN :
0-9761319-1-9
