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The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries

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The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries. / Crone, Sven F.; Kourentzes, Nikolaos.

The 2011 International Joint Conference on Neural Networks (IJCNN). New York : IEEE, 2011. p. 3285-3292.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Crone, SF & Kourentzes, N 2011, The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries. in The 2011 International Joint Conference on Neural Networks (IJCNN). IEEE, New York, pp. 3285-3292, International Joint Conference on Neural Networks (IJCNN), San Jose, 31/07/11. https://doi.org/10.1109/IJCNN.2011.6033657

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Crone, Sven F. ; Kourentzes, Nikolaos. / The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries. The 2011 International Joint Conference on Neural Networks (IJCNN). New York : IEEE, 2011. pp. 3285-3292

Bibtex

@inproceedings{544d06956ef84dd685d1b8b56edf6c9c,
title = "The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries",
abstract = "Forecasting future electricity load represents one of the most prominent areas of electrical engineering, in which artificial neural networks (NN) are routinely applied in practice. The common approach to overcome the complexity of building NNs for high-frequency load data is to segment the time series into simpler and more homogeneous subseries, e. g. seven subseries of hourly loads of only Mondays, Tuesdays etc. These are forecasted independently, using a separate NN model, and then recombined to provide a complete trace forecast for the next days ahead. Despite the empirical importance of load forecasting, and the high operational cost associated with forecast errors, the potential benefits of segmenting time series into subseries have not been evaluated in an empirical comparison. This paper assesses the accuracy of segmenting continuous time series into daily subseries, versus forecasting the original, continuous time series with NNs. Accuracy on hourly UK load data is provided in a valid experimental design, using multiple rolling time origins and robust error metrics in comparison to statistical benchmark algorithms. Results indicate the superior performance of NN on continuous, non-segmented time series, in contrast to best practices in research, practice and software implementations.",
author = "Crone, {Sven F.} and Nikolaos Kourentzes",
year = "2011",
doi = "10.1109/IJCNN.2011.6033657",
language = "English",
isbn = "978-1-4244-9636-5",
pages = "3285--3292",
booktitle = "The 2011 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",
note = "International Joint Conference on Neural Networks (IJCNN) ; Conference date: 31-07-2011 Through 05-08-2011",

}

RIS

TY - GEN

T1 - The impact of preprocessing on forecasting electrical load: an empirical evaluation of segmenting time series into subseries

AU - Crone, Sven F.

AU - Kourentzes, Nikolaos

PY - 2011

Y1 - 2011

N2 - Forecasting future electricity load represents one of the most prominent areas of electrical engineering, in which artificial neural networks (NN) are routinely applied in practice. The common approach to overcome the complexity of building NNs for high-frequency load data is to segment the time series into simpler and more homogeneous subseries, e. g. seven subseries of hourly loads of only Mondays, Tuesdays etc. These are forecasted independently, using a separate NN model, and then recombined to provide a complete trace forecast for the next days ahead. Despite the empirical importance of load forecasting, and the high operational cost associated with forecast errors, the potential benefits of segmenting time series into subseries have not been evaluated in an empirical comparison. This paper assesses the accuracy of segmenting continuous time series into daily subseries, versus forecasting the original, continuous time series with NNs. Accuracy on hourly UK load data is provided in a valid experimental design, using multiple rolling time origins and robust error metrics in comparison to statistical benchmark algorithms. Results indicate the superior performance of NN on continuous, non-segmented time series, in contrast to best practices in research, practice and software implementations.

AB - Forecasting future electricity load represents one of the most prominent areas of electrical engineering, in which artificial neural networks (NN) are routinely applied in practice. The common approach to overcome the complexity of building NNs for high-frequency load data is to segment the time series into simpler and more homogeneous subseries, e. g. seven subseries of hourly loads of only Mondays, Tuesdays etc. These are forecasted independently, using a separate NN model, and then recombined to provide a complete trace forecast for the next days ahead. Despite the empirical importance of load forecasting, and the high operational cost associated with forecast errors, the potential benefits of segmenting time series into subseries have not been evaluated in an empirical comparison. This paper assesses the accuracy of segmenting continuous time series into daily subseries, versus forecasting the original, continuous time series with NNs. Accuracy on hourly UK load data is provided in a valid experimental design, using multiple rolling time origins and robust error metrics in comparison to statistical benchmark algorithms. Results indicate the superior performance of NN on continuous, non-segmented time series, in contrast to best practices in research, practice and software implementations.

U2 - 10.1109/IJCNN.2011.6033657

DO - 10.1109/IJCNN.2011.6033657

M3 - Conference contribution/Paper

SN - 978-1-4244-9636-5

SP - 3285

EP - 3292

BT - The 2011 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

CY - New York

T2 - International Joint Conference on Neural Networks (IJCNN)

Y2 - 31 July 2011 through 5 August 2011

ER -