Incremental mining of frequent power consumption patterns from smart meters big data

2016 IEEE Electrical Power and Energy Conference, EPEC 2016

Volumn , Issue , 2016, Pages

  Singh, Shailendra ^a   Yassine, Abdulsalam ^a   Shirmohammadi, Shervin ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; ELECTRIC POWER UTILIZATION; ENERGY UTILIZATION; SMART METERS;

CONSUMPTION PATTERNS; CONTINUOUS PROCESS; DATA STREAM; ENERGY CONSUMPTION DATUM; INCREMENTAL MINING; KEY ELEMENTS; PATTERN GROWTH;

BIG DATA;

EID: 85010506571     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EPEC.2016.7771716     Document Type: Conference Paper

References (11)

1. T. Yu, N. Chawla, S. Simoff, Computational Intelligent Data Analysis for Sustainable Development, Chapman and Hall/ CRC, Chapter 7, 2013 2013-07-20 2013-07-30 2013-08-09 2013-08-19 2013-08-29 0 100 200 300 400 500 600 700 800 900 Period (Dates) Power Consumption (Watts) Fig. 6. Energy consumption: Washing Machine
2. D. Schweizer, M. Zehnder, H. Wache, H. F. Witschel, Using consumer behavior data to reduce energy consumption in smart homes, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, pp 1123-1129 London, UK: IEEE, 2015
3. J. Han, M. Kamber, J. Pei, Mining Frequent Patterns without Candidate Generation, In: Proc. Conf. on the Management of Data (SIGMOD00, Dallas, TX), pp 1-12. NY, USA : ACM Press, 2000
4. J. Han, M. Kamber, J. Pei, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, 8, pp 53-87, 2004. Netherlands : Kluwer Academic Publishers, 2004
5. J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, (Third Edition), Chapter 6, pp 243-278. San Francisco, USA: Morgan Kaufmann Publishers (Elsevier), 2012
6. R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules, In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB94), Santiago, Chile. San Francisco, USA: Morgan Kaufmann Publishers (Elsevier), 1994
7. J. Kelly, W. Knottenbelt, The UK-DALE dataset, domestic appliancelevel electricity demand and whole-house demand from five UK homes., Sci. Data 2:150007 doi: 10.1038/sdata.2015.7. London, UK: Scientific Data, 2015
8. M. Hassani, C. Beecks, , D. Tws, T. Seidl, Mining Sequential Patterns of Event Streams in a Smart Home Application, Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, Trier, Germany: http://ceur-ws.org, 2015
9. Y. Chen, H. Hung, B. Chiang, S. Peng, P. Chen, Incrementally Mining Usage Correlations among Appliances in Smart Homes, 2015 18th International Conference on Network-Based Information Systems Taipei, Taiwan, pp 273-279 : IEEE, 2015
10. S. Rollins, N. Banerjee, Using Rule Mining to Understand Appliance Energy Consumption Patterns, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), Budapest, Hungry, pp 29-37 : IEEE, 2014
11. S.Rollins, N. Banerjee, Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data, Energies 2015, 8(7), 7407-7427; doi:10.3390/en8077407 Basel, Switzerland : Energies, MDPI, 2015.