Ringwood, John and Austin, Paul C. and Monteith, W. (1993) Forecasting weekly electricity consumption: a case study. Energy Economics, 15 (4). pp. 285-296. ISSN 0140-9883

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Abstract

This paper describes the application of time-series modelling techniques to electricity consumption data for a particular power board. Modelling is performed on total consumption, the data being available on a weekly basis with exact measurements for approximately the past 11 years. Both unforced and forced models are considered. An initial data analysis is performed to ascertain the influence of temperature and rainfall inputs on the model, and later on, a spectral analysis is used to investigate the frequency components present in the time-series data. A significant component of the determination of time-series models is the selection of an appropriate model order. Both low and high order models are evaluated, and their properties compared. For the unforced case, both AR (autoregressive) and ARMA (autoregressive moving average) models are considered. For the forced case, these model structures are extended to include ARX and ARMAX models which have one or more exogenous inputs. Such models are further extended by considering the possibility of predicting the inputs to the models, when a forecasting approach is required. Simulation results are provided for all cases together with a measure of the prediction accuracy. Comparisons are made for the various model structures, as well as models based on short and long data records and models which are driven with an external noise sequence or merely released from appropriate initial conditions.

Item Type:	Article
Keywords:	Forecasting; Time series; Electricity consumption;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	9554
Identification Number:	https://doi.org/10.1016/0140-9883(93)90018-M
Depositing User:	Professor John Ringwood
Date Deposited:	14 Jun 2018 15:26
Journal or Publication Title:	Energy Economics
Publisher:	Elsevier
Refereed:	Yes
URI:	https://www.sciencedirect.com
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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