publication . Article . 2015

Rule Induction-Based Knowledge Discovery for Energy Efficiency

Qipeng Chen; Zhong Fan; Dritan Kaleshi; Simon Armour;
Open Access English
  • Published: 01 Sep 2015
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induction techniques are applied to derive knowledge from a dataset of thousands of Irish electricity customers’ time-series power consumption records, socio-demographic details, ...
Subjects
free text keywords: Energy efficiency, knowledge discovery, smart grids, subgroup discovery, QA75
Related Organizations
31 references, page 1 of 3

[1] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, ``From data mining to knowledge discovery in databases,'' AI Mag., vol. 17, no. 3, pp. 37 54, 1996.

[2] S. Greco, B. Matarazzo, R. Slowinski, and J. Stefanowski, ``An algorithm for induction of decision rules consistent with the dominance principle,'' in Rough Sets and Current Trends in Computing (Lecture Notes in Computer Science), W. Ziarko and Y. Yao, Eds. Berlin, Germany: Springer-Verlag, 2001, pp. 304 313.

[3] J. Fürnkranz, ``Separate-and-conquer rule learning,'' Artif. Intell. Rev., vol. 13, no. 1, pp. 3 54, 1999. [OpenAIRE]

[4] N. Lavra£, B. Kav²ek, P. Flach, and L. Todorovski, ``Subgroup discovery with CN2-SD,'' J. Mach. Learn. Res., vol. 5, pp. 153 188, Feb. 2004.

[5] I. MacLeay et al., ``Digest of United Kingdom energy statistics 2014,'' Dept. Energy Climate Change, London, U.K., Tech. Rep. ISBN 9780115155307, 2014.

[6] R. de Sá Ferreira, L. A. Barroso, P. R. Lino, M. M. Carvalho, and P. Valenzuela, ``Time-of-use tariff design under uncertainty in priceelasticities of electricity demand: A stochastic optimization approach,'' IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 2285 2295, Dec. 2013.

[7] S. Gottwalt, W. Ketter, C. Block, J. Collins, and C. Weinhardt, ``Demand side management A simulation of household behavior under variable prices,'' Energy Policy, vol. 39, no. 12, pp. 8163 8174, 2011.

[8] G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader, ``Customer characterization options for improving the tariff offer,'' IEEE Trans. Power Syst., vol. 18, no. 1, pp. 381 387, Feb. 2003. [OpenAIRE]

[9] F. McLoughlin, A. Duffy, and M. Conlon, ``Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study,'' Energy Buildings, vol. 48, pp. 240 248, May 2012.

[10] T. A. Nguyen and M. Aiello, ``Energy intelligent buildings based on user activity: A survey,'' Energy Buildings, vol. 56, pp. 244 257, Jan. 2013. [OpenAIRE]

[11] S. Darby, ``The effectiveness of feedback on energy consumption. A review for Defra of the literature on metering, billing and direct displays,'' Environ. Change Inst., Oxford, U.K., Apr. 2006.

[12] H. Allcott, ``Social norms and energy conservation,'' J. Public Econ., vol. 95, nos. 9 10, pp. 1082 1095, 2011. [OpenAIRE]

[13] H. S. Cho, T. Yamazaki, and M. Hahn, ``AERO: Extraction of user's activities from electric power consumption data,'' IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 2011 2018, Aug. 2010.

[14] Y. G. Yohanis, ``Domestic energy use and householders' energy behaviour,'' Energy Policy, vol. 41, pp. 654 665, Feb. 2012. [OpenAIRE]

[15] A. Nilsson, C. J. Bergstad, L. Thuvander, D. Andersson, K. Andersson, and P. Meiling, ``Effects of continuous feedback on households' electricity consumption: Potentials and barriers,'' Appl. Energy, vol. 122, pp. 17 23, Jun. 2014.

31 references, page 1 of 3
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue