Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -