Fay, Damien and Ringwood, John and Condon, Marissa and Kelly, Michael (2000) A comparison of linear and neural parallel time series models for short-term load forecasting in the Republic of Ireland. Proceedings of the 3rd European IFS Workshop.

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Abstract

This paper presents a comparison between parallel linear and parallel neural network models. Parallel models consist of 24 separate models, one for each hour of the day. Each parallel model decomposes the load into a linear Auto- Regressive (AR) part and a residual. Exogenous linear and neural network model performance is compared in predicting this residual. Three days or 72 hours of current and delayed weather variables are available as exogenous inputs for the residual models. Input selection comprises of testing the bootstrapped performance of a linear model. The inputs are ordered using 4 methods derived from a mix of the T-ratio of the linear coefficients and Principal Component Analysis (PCA). The neural network models are found to give superior results due to the non-linear AR nature of the residual

Item Type:	Article
Keywords:	linear and neural parallel time series models; short-term load forecasting;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	1913
Depositing User:	Professor John Ringwood
Date Deposited:	15 Apr 2010 15:39
Journal or Publication Title:	Proceedings of the 3rd European IFS Workshop
Publisher:	Shaker Verlag
Refereed:	Yes
URI:
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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