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All issues Volume 76 (2016) MATEC Web Conf., 76 (2016) 04023 References
Open Access
Issue
MATEC Web Conf.
Volume 76, 2016
20th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
Article Number 04023
Number of page(s) 7
Section Computers
DOI https://doi.org/10.1051/matecconf/20167604023
Published online 21 October 2016
  1. H. Sui, et al. An AMI system for the deregulated electricity markets, IEEE Transactions on Industry Applications 45.6, 2104–2108 (2009) [Google Scholar]
  2. M. Popa, Data collecting from smart meters in an Advanced Metering Infrastructure, 15th IEEE International Conference on Intelligent Engineering Systems (INES), (2011) [Google Scholar]
  3. Y. Simmhan, et al. Cloud-based software platform for big data analytics in smart grids, Computing in Science & Engineering, 15.4, 38–47 (2013) [CrossRef] [Google Scholar]
  4. D. Alahakoon, X. Yu, Advanced analytics for harnessing the power of smart meter big data, IEEE International Workshop on Intelligent Energy Systems (IWIES), (2013) [Google Scholar]
  5. M. Çunkaş, U. Taşkiran, Turkey’s electricity consumption forecasting using genetic programming, Energy Sources, Part B: Economics, Planning, and Policy 6.4, 406–416 (2011) [Google Scholar]
  6. T. Räsänen, et al. Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data, Applied Energy 87.11, 3538–3545 (2010) [CrossRef] [Google Scholar]
  7. S. Humeau, et al. Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households, IEEE Sustainable Internet & ICT for Sustainability (SustainIT), (2013) [Google Scholar]
  8. Y. Goude, R. Nedellec, N. Kong, Local short and middle term electricity load forecasting with semi-parametric additive models, IEEE Transactions on Smart Grid, 5.1, 440–446 (2014) [CrossRef] [Google Scholar]
  9. Y. Simmhan, M. U. Noor, Scalable prediction of energy consumption using incremental time series clustering, IEEE International Conference on Big Data, (2013) [Google Scholar]
  10. D. De Silva, et al. A data mining framework for electricity consumption analysis from meter data, IEEE Transactions on Industrial Informatics 7.3, 399–407 (2011) [CrossRef] [Google Scholar]
  11. D. Mashima, A. A. Cárdenas, Evaluating electricity theft detectors in smart grid networks, Research in Attacks, Intrusions, and Defenses, Springer Berlin Heidelberg. 210–229 (2012) [CrossRef] [Google Scholar]
  12. H. Li, et al. Usage analysis for smart meter management, 8th IEEE International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT), (2011) [Google Scholar]
  13. K. Y. Khumchoo, W. Kongprawechnon, Cluster analysis for primary feeder identification using metering data, 6th IEEE International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), (2015) [Google Scholar]
  14. I. Dent, U. Aickelin, T. Rodden, Application of a clustering framework to UK domestic electricity data, arXiv preprint arXiv, 1307.1079 (2013) [Google Scholar]
  15. M. Espinoza, et al. Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series, IEEE Transactions on Power Systems, 20.3, 1622–1630 (2005) [CrossRef] [Google Scholar]
  16. F. McLoughlin, A. Duffy, M. Conlon, Evaluation of time series techniques to characterise domestic electricity demand, Energy 50, 120–130 (2013) [CrossRef] [Google Scholar]
  17. J. Widén, et al. Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation, Energy and Buildings 41.7, 753–768 (2009) [CrossRef] [Google Scholar]
  18. E. C. Bobric, G. Cartina, G. Grigoras, Clustering techniques in load profile analysis for distribution stations, Advances in Electrical and Computer Engineering 9.1, 63–66 (2009) [CrossRef] [Google Scholar]
  19. K. H. Tiedemann, Using conditional demand analysis to estimate residential energy use and energy savings, Proceedings of the CDEEE (2007) [Google Scholar]
  20. T. M. Lai, et al. Electricity consumption in Hong Kong: trend analysis and greenhouse gases emission, HKIE Transactions, 21.2, 81–88 (2014) [CrossRef] [Google Scholar]
  21. H. Sæle, O. S. Grande, Demand response from household customers: experiences from a pilot study in Norway, IEEE Transactions on Smart Grid 2.1, 102–109 (2011) [CrossRef] [Google Scholar]
  22. T. Räsänen, J. Ruuskanen, M. Kolehmainen., Reducing energy consumption by using self-organizing maps to create more personalized electricity use information, Applied Energy 85.9, 830–840 (2008) [CrossRef] [Google Scholar]
  23. D. Gerbec, S. Gašperič, F. Gubina, Allocation of the load profiles to consumers using probabilistic neural networks, IEEE Transactions on Power Systems, 20.2, 548–555 (2005) [CrossRef] [Google Scholar]
  24. D. Gerbec, et al. Determining the load profiles of consumers based on fuzzy logic and probability neural networks, IEEE Proceedings-Generation, Transmission & Distribution 151.3, 395–400 (2004) [CrossRef] [Google Scholar]
  25. F. McLoughlin, A. Duffy, M. Conlon, A clustering approach to domestic electricity load profile characterisation using smart metering data, Applied energy, 141, 190–199 (2015) [CrossRef] [Google Scholar]
  26. C. Beckel, L. Sadamori, S. Santini, Towards automatic classification of private households using electricity consumption data, Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, (2012) [Google Scholar]
  27. S. V. Verdú, et al. Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps, IEEE Transactions on Power Systems 21.4, 1672–1682 (2006) [CrossRef] [Google Scholar]
  28. S. V. Verdű, et al. Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters, Power Systems Conference and Exposition, IEEE PES, (2004) [Google Scholar]
  29. E. Carpaneto, et al. Electricity customer classification using frequency–domain load pattern data, Int. J. of Electrical Power & Energy Systems 28.1, 13–20 (2006) [CrossRef] [Google Scholar]
  30. G. Chicco, et al. Load pattern-based classification of electricity customers, IEEE Transactions on Power Systems 19.2, 1232–1239 (2004) [CrossRef] [Google Scholar]
  31. D. Gerbec, S. Gašperič, F. Gubina, An approach to customers daily load profile determination, IEEE Power Engineering Society Summer Meeting Vol. 1, (2002) [Google Scholar]
  32. D. Gerbec, S. Gašperič, F. Gubina, Determination and allocation of typical load profiles to the eligible consumers, IEEE Power Tech Conference Proceedings, Bologna. Vol. 1, (2003) [Google Scholar]
  33. R. Yao, K. Steemers, A method of formulating energy load profile for domestic buildings in the UK, Energy and Buildings 37.6, 663–671 (2005) [CrossRef] [Google Scholar]
  34. S. W. Heunis, R. Herman, A probabilistic model for residential consumer loads, IEEE Transactions on Power Systems 17.3, 621–625 (2002) [CrossRef] [Google Scholar]
  35. O. Ardakanian, et al. Computing Electricity Consumption Profiles from Household Smart Meter Data, EDBT/ICDT Workshops, (2014) [Google Scholar]
  36. S. Prasad, S. B. Avinash, Smart meter data analytics using OpenTSDB and Hadoop, IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), (2013) [Google Scholar]
  37. J. Daemen, and V. Rijmen, The design of Rijndael: AES-the advanced encryption standard, Springer Science & Business Media, (2013) [Google Scholar]
  38. R Documentation, https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html [Google Scholar]
  39. R package for detecting unusual time series, http://robjhyndman.com/hyndsight/anomalous/ [Google Scholar]
  40. R. J. Hyndman, et al. Large-scale unusual time series detection, Proceedings of International Conference on Data Mining series, (2015) [Google Scholar]
  41. W. M. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association 66 (336), 846–850 (1971) [Google Scholar]
  42. D. M. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, (2011) [Google Scholar]

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