Kourentzes, Nikolaos and Crone, Sven F. (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. ISBN 978-1-4244-9636-5Full text not available from this repository.
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.
|Item Type:||Contribution in Book/Report/Proceedings|
|Subjects:||H Social Sciences > HB Economic Theory|
|Departments:||Lancaster University Management School > Management Science|
|Deposited On:||24 Jul 2012 14:51|
|Last Modified:||05 Feb 2016 02:03|
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