Journal article
A comparison of univariate methods for forecasting electricity demand up to a day ahead.
- Abstract:
- This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
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-
(Preview, Accepted manuscript, pdf, 293.8KB, Terms of use)
-
- Publisher copy:
- 10.1016/j.ijforecast.2005.06.006
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funding agency for:
- McSharry, P
- Publisher:
- Elsevier
- Journal:
- International Journal of Forecasting More from this journal
- Volume:
- 22
- Issue:
- 1
- Pages:
- 1 - 16
- Publication date:
- 2006-01-01
- DOI:
- ISSN:
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0169-2070
- Language:
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English
- UUID:
-
uuid:21b59bfb-731a-4279-9375-bd7c0eaf02bb
- Local pid:
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oai:economics.ouls.ox.ac.uk:14877
- Deposit date:
-
2011-08-16
- ARK identifier:
Terms of use
- Copyright holder:
- International Institute of Forecasters
- Copyright date:
- 2006
- Notes:
- Copyright 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. NOTICE: this is the author's version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 22, 1, (January-March 2006) DOI#10.1016/j.ijforecast.2005.06.006
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