]> 2026-04-04T21:50:48+02:00 Anomaly ranking of District Heating Substations 0 en Master Implementing anomaly ranking algorithm to monitor district heating substations. Artificial Intelligence and Learning Systems courses; good knowledge of data mining; programming skills for implementing machine learning algorithms M. 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. Open Anomaly ranking of District Heating Substations 2018-09-23T18:41:02Z 2458385.2784954 Anomaly ranking of District Heating Substations 0 [2,"smw-datavalue-property-restricted-annotation-use","Keywords"] Anomaly ranking of District Heating Substations# ERRd9c33d5ac8a89308063e13c4b212fa7c