Mining anomalous electricity consumption using Ensemble Empirical Mode Decomposition

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 5238-5242

  Fontugne, Romain ^a,b   Tremblay, Nicolas ^c   Borgnat, Pierre ^c   Flandrin, Patrick ^c   Esaki, Hiroshi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b National Institute of Informatics   (Japan)

^c UNIVERSITÉ DE LYON   (France)

Author keywords

Anomaly Detection; Empirical Mode Decomposition; Energy Consumption

Indexed keywords

ANOMALY DETECTION; DATA STREAM; ELECTRICITY-CONSUMPTION; EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; LARGE BUILDINGS; SENSOR DEPLOYMENT;

ENERGY UTILIZATION; SENSORS;

SIGNAL PROCESSING;

EID: 84890498384     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638662     Document Type: Conference Paper

References (12)

1. Gowtham Bellala, Manish Marwah, Martin Arlitt, Geoff Lyon, and Cullen E. Bash, "Towards an understanding of campusscale power consumption," Seattle, WA, Nov. 1, 2011, Buildsys'11, p. 6.
2. Michael Wrinch, Tarek H.M. EL-Fouly, and Steven Wong, "Anomaly detection of building systems using energy demand frequency domain anlaysis," in IEEE Power &Energy Society General Meeting, San-Diego, CA, USA, 2012.
3. Chao Chen and Diane J. Cook, "Energy outlier detection in smart environments.," in Artificial Intelligence and Smarter Living. 2011, vol. WS-11-07 of AAAI Workshops, AAAI.
4. Srinivas Katipamula and Michael R. Brambley, "Methods for fault detection, diagnostics, and prognostics for building systems, a review, part I," HVAC&R Research, vol. 11, no. 1, pp. 3-25, 2005.
5. Srinivas Katipamula and Michael R. Brambley, "Methods for fault detection, diagnostics, and prognostics for building systems, a review, part II," HVAC&R Research, vol. 11, no. 2, pp. 169-187, 2005.
6. John E. Seem, "Using intelligent data analysis to detect abnormal energy consumption in buildings," Energy and Buildings, vol. 39, no. 1, pp. 52-58, 2007.
7. Zhaohua Wu and Norden E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, no. 01, pp. 1-41, 2009.
8. Maria E. Torres, Marcelo A. Colominas, Gaston Schlotthauer, and Patrick Flandrin, "A complete ensemble empirical mode decomposition with adaptive noise," in Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE, 2011, pp. 4144-4147.
9. Romain Fontugne, Jorge Ortiz, David Culler, and Hiroshi Esaki, "Empirical mode decomposition for intrinsicrelationship extraction in large sensor deployments," in IoTApp' 12, Workshop on Internet of Things Applications, Beijing, China, 2012.
10. Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H. Liu, "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A, vol. 454, no. 1971, pp. 903-995, 1998.
11. Peter J. Huber and Elvezio M. Ronchetti, Robust Statistics, Wiley Series in Probability and Statistics. Wiley, 2009.
12. Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre, "Fast unfolding of communities in large networks," J.STAT.MECH., 2008.