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Semi-supervised monitoring of electric load time series for unusual patterns

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

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Semi-supervised monitoring of electric load time series for unusual patterns. / Kourentzes, Nikolaos; Crone, Sven F.

The 2011 International Joint Conference on Neural Networks (IJCNN). New York : IEEE, 2011. p. 2852-2859.

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

Harvard

Kourentzes, N & Crone, SF 2011, Semi-supervised monitoring of electric load time series for unusual patterns. in The 2011 International Joint Conference on Neural Networks (IJCNN). IEEE, New York, pp. 2852-2859, International Joint Conference on Neural Networks (IJCNN), San Jose, 31/07/11. https://doi.org/10.1109/IJCNN.2011.6033595

APA

Kourentzes, N., & Crone, S. F. (2011). Semi-supervised monitoring of electric load time series for unusual patterns. In The 2011 International Joint Conference on Neural Networks (IJCNN) (pp. 2852-2859). IEEE. https://doi.org/10.1109/IJCNN.2011.6033595

Vancouver

Kourentzes N, Crone SF. Semi-supervised monitoring of electric load time series for unusual patterns. In The 2011 International Joint Conference on Neural Networks (IJCNN). New York: IEEE. 2011. p. 2852-2859 https://doi.org/10.1109/IJCNN.2011.6033595

Author

Kourentzes, Nikolaos ; Crone, Sven F. / Semi-supervised monitoring of electric load time series for unusual patterns. The 2011 International Joint Conference on Neural Networks (IJCNN). New York : IEEE, 2011. pp. 2852-2859

Bibtex

@inproceedings{b55402eea87e48de9688f19e05926f65,
title = "Semi-supervised monitoring of electric load time series for unusual patterns",
abstract = "In this paper we propose a semi-supervised neural network algorithm to identify unusual load patterns in hourly electricity demand time series. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns in such series. This becomes more important considering the difficulty and the cost associated with manual exploration of such data, due to the vast number of observations. The proposed network learns from both labeled and unlabeled patterns, adapting automatically as more data become available. This drastically limits the cost and effort associated with exploring and labeling such data. We compare the proposed method with conventional supervised and unsupervised approaches, demonstrating higher accuracy, robustness and efficacy on empirical electricity load data.",
author = "Nikolaos Kourentzes and Crone, {Sven F.}",
year = "2011",
doi = "10.1109/IJCNN.2011.6033595",
language = "English",
isbn = "978-1-4244-9636-5",
pages = "2852--2859",
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 - Semi-supervised monitoring of electric load time series for unusual patterns

AU - Kourentzes, Nikolaos

AU - Crone, Sven F.

PY - 2011

Y1 - 2011

N2 - In this paper we propose a semi-supervised neural network algorithm to identify unusual load patterns in hourly electricity demand time series. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns in such series. This becomes more important considering the difficulty and the cost associated with manual exploration of such data, due to the vast number of observations. The proposed network learns from both labeled and unlabeled patterns, adapting automatically as more data become available. This drastically limits the cost and effort associated with exploring and labeling such data. We compare the proposed method with conventional supervised and unsupervised approaches, demonstrating higher accuracy, robustness and efficacy on empirical electricity load data.

AB - In this paper we propose a semi-supervised neural network algorithm to identify unusual load patterns in hourly electricity demand time series. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns in such series. This becomes more important considering the difficulty and the cost associated with manual exploration of such data, due to the vast number of observations. The proposed network learns from both labeled and unlabeled patterns, adapting automatically as more data become available. This drastically limits the cost and effort associated with exploring and labeling such data. We compare the proposed method with conventional supervised and unsupervised approaches, demonstrating higher accuracy, robustness and efficacy on empirical electricity load data.

U2 - 10.1109/IJCNN.2011.6033595

DO - 10.1109/IJCNN.2011.6033595

M3 - Conference contribution/Paper

SN - 978-1-4244-9636-5

SP - 2852

EP - 2859

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 -