Anomaly detection for visual analytics of power consumption data


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JANETZKO, Halldór, Florian STOFFEL, Sebastian MITTELSTÄDT, Daniel A. KEIM, 2014. Anomaly detection for visual analytics of power consumption data. In: Computers & Graphics. 38, pp. 27-37. ISSN 0097-8493. Available under: doi: 10.1016/j.cag.2013.10.006

@article{Janetzko2014Anoma-26217, title={Anomaly detection for visual analytics of power consumption data}, year={2014}, doi={10.1016/j.cag.2013.10.006}, volume={38}, issn={0097-8493}, journal={Computers & Graphics}, pages={27--37}, author={Janetzko, Halldór and Stoffel, Florian and Mittelstädt, Sebastian and Keim, Daniel A.} }

eng Commercial buildings are significant consumers of electrical power. Also, energy expenses are an increasing cost factor. Many companies therefore want to save money and reduce their power usage. Building administrators have to first understand the power consumption behavior, before they can devise strategies to save energy. Second, sudden unexpected changes in power consumption may hint at device failures of critical technical infrastructure. The goal of our research is to enable the analyst to understand the power consumption behavior and to be aware of unexpected power consumption values. In this paper, we introduce a novel unsupervised anomaly detection algorithm and visualize the resulting anomaly scores to guide the analyst to important time points. Different possibilities for visualizing the power usage time series are presented, combined with a discussion of the design choices to encode the anomaly values. Our methods are applied to real-world time series of power consumption, logged in a hierarchical sensor network. Janetzko, Halldór Keim, Daniel A. Computers & Graphics ; 38 (2014). - S. 27-37 terms-of-use Stoffel, Florian 2014 Mittelstädt, Sebastian 2014-02-05T09:00:55Z Anomaly detection for visual analytics of power consumption data Stoffel, Florian Keim, Daniel A. 2014-02-05T09:00:55Z Mittelstädt, Sebastian Janetzko, Halldór

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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