Statistical anomaly detection in mean and variation of energy consumption

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2014, Pages 3570-3575

  Chen, Bei ^a   Sinn, Mathieu ^a   Ploennigs, Joern ^a   Schumann, Anika ^a  

^a IBM RESEARCH   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

ARCHES; ELECTRIC LOAD MANAGEMENT; ENERGY EFFICIENCY; ENERGY UTILIZATION; PATTERN RECOGNITION; STATISTICAL METHODS;

ANOMALY-DETECTION ALGORITHMS; BASELINE CONSUMPTION LEVELS; BUILDING ENERGY CONSUMPTION; GENERALIZED ADDITIVE MODEL; PREDICTION INTERVAL; PREDICTIVE METHODS; STATISTICAL ANOMALY DETECTION; TIME DEPENDENT VARIATIONS;

ANOMALY DETECTION;

EID: 84919933375     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.614     Document Type: Conference Paper

References (19)

1. World energy outlook 2012. International Energy Agency, 2012
2. H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6)
3. T. Andersen and T. Bollerslev. Answering the sceptics: yes standard volatility models do provide accurate forecasts. International Economic Review, 39:885-905, 1998
4. A. Azadeh, S. Ghaderi, S. Tarverdian, and M. Saberi. Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied Mathematics and Computation, 186(2):1731-1741, Mar. 2007
5. A. Ba, M. Sinn, Y. Goude, and P. Pompey. Adaptive learning of smoothing functions: Application to electricity load forecasting. In Advances in Neural Information Processing Systems (NIPS), 2012
6. T. Bollerslev. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31:307-327, 1986
7. B. Chen. Linerization Methods in Time Theory Analysis. PhD Dissertation, University of Waterloo, Canada, 2011
8. B. Chen, Y. Gel, N. Balakrishna, and B. Abraham. Generalized autoregressive conditional heteroskedasticity. Journal of Forecasting, 30:51-71, 2011
9. R. F. Engle. Autoregressive conditional heteroscedasticity with estimates of variance of united kingdom inflation. Econometrica, 50:987-1008, 1982
10. H. Hippert, C. Pedreira, and R. Souza. Neural networks for short-term load forecasting: A review and evaluation. 16(1):44-55, 2001
11. G. J. Kendall, M. G. Rank Correlation Methods. 5th edition, Griffin, London, 1990
12. K. Lee, Y. T. Cha, and J. Park. Short-term load forecasting using an artificial neural network. 7(1):124-132, 1992
13. J. Miguel and P. Olave. Bootstrapping forecast intervals in arch models. Test, 8(2):345-364, 1999
14. E. Page. Continuous inspection scheme. Biometrika, 41:100-115, 1954
15. P.-F. Pai and W.-C. Hong. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management, 46(17):2669-2688, Oct. 2005
16. J. Ploennigs, B. Chen, A. Schumann, and N. Brady. Exploiting generalized additive models for diagnosing abnormal energy use in buildings. In The 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (BuildSys'13), 2013
17. R. Tibshirani and T. Hastie. Generalized additive models. Chapman & Hall, London, 1990
18. S. Wood. Generalized additive models: An Introduction with R. Chapman & Hall, London, 2006
19. H. Zhao and F. Magoulès. Parallel support vector machines applied to the prediction of multiple buildings energy consumption. Journal of Alg. & Comput. Techn., 4(2):231-249, June 2010.