Anomaly detection for visual analytics of power consumption data

Computers and Graphics (Pergamon)

Volumn 38, Issue 1, 2014, Pages 27-37

  Janetzko, Halldór ^a   Stoffel, Florian ^a   Mittelstädt, Sebastian ^a   Keim, Daniel A ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Anomaly detection; Importance driven; Pixel based visualization; Visual analytics

Indexed keywords

ELECTRIC POWER UTILIZATION; OFFICE BUILDINGS; SENSOR NETWORKS; TIME SERIES; VISUALIZATION;

COMMERCIAL BUILDING; CONSUMPTION VALUES; HIERARCHICAL SENSOR NETWORK; IMPORTANCE-DRIVEN; REAL-WORLD TIME SERIES; TECHNICAL INFRASTRUCTURE; UNSUPERVISED ANOMALY DETECTION; VISUAL ANALYTICS;

ANOMALY DETECTION;

EID: 84887831482     PISSN: 00978493     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cag.2013.10.006     Document Type: Article

References (38)

1. United states energy information administration. Annual energy review, 2010.
2. US department of energy. Energy efficiency trends in residential and commercial buildings; 2008. a http://apps1.eere.energy.gov/buildings/ publications/pdfs/corporate/bt-stateindustry.pdf a.
3. W. Aigner, S. Miksch, H. Schumann, and C. Tominski Time & time-oriented data. Human-computer interaction series 2011 Springer London ISBN 978-0-85729-078-6
4. Agarwal Y, Weng T, Gupta RK. The energy dashboard: improving the visibility of energy consumption at a campus-wide scale. In: Proceedings of the first ACM workshop on embedded sensing systems for energy-efficiency in buildings. ACM; 2009. p. 55-60.
5. V.M. Catterson, S.D. McArthur, and G. Moss Online conditional anomaly detection in multivariate data for transformer monitoring IEEE Trans Power Deliv 25 4 2010 2556 2564
6. S.D. McArthur, C.D. Booth, J. McDonald, and I.T. McFadyen An agent-based anomaly detection architecture for condition monitoring IEEE Trans Power Syst 20 4 2005 1675 1682 (Pubitemid 41700519)
7. Jakkula V, Cook D. Outlier detection in smart environment structured power datasets. In: 6th International conference on intelligent environments (IE), 2010. IEEE; 2010. p. 29-33.
8. J.E. Seem Using intelligent data analysis to detect abnormal energy consumption in buildings Energy Build 39 1 2007 52 58 (Pubitemid 44827565)
9. J.L. Mathieu, P.N. Price, S. Kiliccote, and M.A. Piette Quantifying changes in building electricity use, with application to demand response IEEE Trans Smart Grid 2 3 2011 507 518
10. H.x. Zhao, and F. Magoulès A review on the prediction of building energy consumption Renew Sustain Energy Rev 16 6 2012 3586 3592
11. M.C. Hao, H. Janetzko, S. Mittelstädt, W. Hill, U. Dayal, and D.A. Keim A visual analytics approach for peak-preserving prediction of large seasonal time series Comput Graph Forum 30 3 2011 691 700
12. A http://en.wikipedia.org/wiki/Energy-in-the-United-States a[viewed 17.06.2013].
13. IBM TRIRIGA Energy Optimization: integrated software solution for improving buildings management and facilities operation. a http://pic.dhe.ibm. com/infocenter/tivihelp/v57r1/topic/com.ibm.iteo.doc/infocenter.pdf a.[viewed 17.06.2013]
14. UCEI (University of California Energy Institute), Berkeley, California, CA. New york city building energy map; 2007. a http://www.visualizing.org/ visualizations/new-york-city-building-energy-map a.
15. Granderson J, Piette MA, Ghatikar G, Price P. Building energy information systems: state of the technology and user case studies. In: Handbook of web based energy information and control systems, 2009.
16. IBM Research and the IBM Cognos Software Group. Many eyes: public building energy consumptions, 2013.
17. Google PowerMeter. a http://www.google.com/powermeter/about/ a[viewed 17.06.2013].
18. Shi L, Liao Q, He Y, Li R, Striegel A, Su Z. SAVE: sensor anomaly visualization engine. In: 2011 IEEE conference on visual analytics science and technology (VAST). IEEE; 2011. p. 201-10.
19. Buevich M, Rowe A, Rajkumar R. SAGA: tracking and visualization of building energy. In: 2011 IEEE 17th international conference on embedded and real-time computing systems and applications (RTCSA), vol. 2. IEEE; 2011. p. 31-6.
20. Burch M, Weiskopf D. Visualizing dynamic quantitative data in hierarchies. In: Proceedings of international conference on information visualization theory and applications; 2011. p. 177-86.
21. Bellala G, Marwah M, Arlitt M, Lyon G, Bash C. Following the electrons: methods for power management in commercial buildings. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM; 2012. p. 994-1002.
22. Bellala G, Marwah M, Arlitt M, Lyon G, Bash CE. Towards an understanding of campus-scale power consumption. In: Proceedings of the 3rd ACM workshop on embedded sensing systems for energy-efficiency in buildings. ACM; 2011. p. 73-8.
23. Keim DA, Ankerst M, Kriegel HP. Recursive pattern: a technique for visualizing very large amounts of data. In: Proceedings of the 6th conference on visualization'95. IEEE Computer Society; 1995. p. 279-86.
24. Lammarsch T, Aigner W, Bertone A, Gartner J, Mayr E, Miksch S, et al. Hierarchical temporal patterns and interactive aggregated views for pixel-based visualizations. In: 2009 13th International conference on information visualisation. IEEE; 2009. p. 44-50.
25. Weber M, Alexa M, Müller W. Visualizing time-series on spirals. In: Proceedings of the IEEE symposium on information visualization, 2001. p. 7-13. (Pubitemid 33099020)
26. D. Oelke, H. Janetzko, S. Simon, K. Neuhaus, and D.A. Keim Visual boosting in pixel-based visualizations Comput Graph Forum 30 3 2011 871 880
27. D.A. Keim Designing pixel-oriented visualization techniques theory and applications IEEE Trans Vis Comput Graph 6 1 2000 59 78 (Pubitemid 30594716)
28. Healey CG. Choosing effective colours for data visualization. In: Proceedings of Visualization'96. IEEE; 1996. p. 263-70.
29. L. Wang, and A. Kaufman Importance driven automatic color design for direct volume rendering Comput Graph Forum 31 3pt4 2012 1305 1314
30. Keim DA, Kriegel HP. Issues in visualizing large databases. In: Proceedings of conference on visual database systems (VDB'95), Lausanne, Schweiz, 1995. p. 203-14.
31. Kosara R, Miksch S, Hauser H. Semantic depth of field. In: Proceedings of IEEE symposium on information visualization 2001 (INFOVIS 2001). IEEE Computer Society Press; 2001. p. 97-104. (Pubitemid 33099032)
32. A. Giusti, P. Taddei, G. Corani, L. Gambardella, C. Magli, and L. Gianaroli Artificial defocus for displaying markers in microscopy z-stacks IEEE Trans Vis Comput Graph 17 12 2011 1757 1764
33. Johnson B, Shneiderman B. Tree-maps: a space-filling approach to the visualization of hierarchical information structures. In: Proceedings of IEEE conference on visualization, 1991 (Visualization'91). IEEE; 1991. p. 284-91.
34. B. Shneiderman Tree visualization with tree-maps 2-d space-filling approach ACM Trans Graph 11 1 1992 92 99
35. Schreck T, Keim DA, Mansmann F. Regular treemap layouts for visual analysis of hierarchical data. In: Proceedings of the spring conference on computer graphics (SCCG'2006). Casta Papiernicka, Slovak Republic: ACM Siggraph; 2006. p. 184-91.
36. Hao MC, Dayal U, Keim DA, Schreck, T. Importance-driven visualization layouts for large time series data. In: IEEE symposium on information visualization, 2005 (INFOVIS 2005). IEEE; 2005. p. 203-10. (Pubitemid 44500057)
37. Bruls M, Huizing K, van Wijk J. Squarified treemaps. In: Proceedings of the joint eurographics and IEEE TCVG symposium on visualization (VisSym'00). Eurographics Association; 2000. p. 33-42.
38. M.J. Baker, and S.G. Eick Space-filling software visualization J Vis Lang Comput 6 2 1995 119 133