Gaussian Process Prior Models for Electrical Load Forecasting

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Leith, Douglas J., Heidl, Martin and Ringwood, John (2004) Gaussian Process Prior Models for Electrical Load Forecasting. Probabilistic Methods Applied to Power Systems. pp. 112-117.

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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.

Item Type:	Article
Keywords:	Gaussian process; basic structural models; electrical load forecasting; electricity demand; seasonal auto-regressive intergrated;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	1938
Depositing User:	Professor John Ringwood
Date Deposited:	19 May 2010 15:57
Journal or Publication Title:	Probabilistic Methods Applied to Power Systems
Publisher:	IEEE
Refereed:	Yes
Related URLs:	http://ieeexplore.ieee.org/Xplore/guesth...
URI:	https://mu.eprints-hosting.org/id/eprint/1938
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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Gaussian Process Prior Models for Electrical Load Forecasting

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