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Anomaly ranking of District Heating Substations
Keywords anomaly detection, self monitoring, data mining, learnin-to-rank  +
Level Master  +
OneLineSummary Implementing anomaly ranking algorithm to monitor district heating substations.  +
Prerequisites Artificial Intelligence and Learning Systems courses; good knowledge of data mining; programming skills for implementing machine learning algorithms  +
References M. Goldstein and S. Uchida, "A ComparativeM. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data", PLOS ONE, vol. 11, no. 4, p. e0152173, 2016. P. Arjunan, H. Khadilkar, T. Ganu, Z. Charbiwala, A. Singh and P. Singh, "Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information", Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15, 2015. D. Araya, K. Grolinger, H. ElYamany, M. Capretz and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption", Energy and Buildings, vol. 144, pp. 191-206, 2017. S. Rayana and L. Akoglu, "Less is More", ACM Transactions on Knowledge Discovery from Data, vol. 10, no. 4, pp. 1-33, 2016. Huang, Huaming, "Rank Based Anomaly Detection Algorithms" (2013). Electrical Engineering and Computer Science - Dissertations.Paper 331.omputer Science - Dissertations.Paper 331.
StudentProjectStatus Open  +
Supervisors Ece Calikus + , Sławomir Nowaczyk +
Title Anomaly ranking of District Heating Substations  +
Categories StudentProject  +
Modification dateThis property is a special property in this wiki. 23 September 2018 18:41:02  +
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