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Multi-attributes Graph Algorithm for Association Rules Mining Over Energy Internet

  • Ling Wang
  • Fu Tao Ma
  • Tie Hua Zhou
  • Xue Gao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)

Abstract

In recent years, with the development of the energy internet. Developing energy internet system is a necessary requirement for building resource-saving and environment-friendly society. Due to the consumption of the load is affected by many factors, each factor is an attribute. Our main contribution is that as the property changes in the weight of all influencing factors in the different time intervals, and calculates the global attribute nodes based on the graph updating. Furthermore, for analysis and predicts the trend of user side power consumption. By this way, our objective is through the definition of various attributes, discovery groups of potential distribution formed by dense power graphs that are homogeneous with respect to the attribute correlation of users. To this aim, we present a new kind of pattern algorithm called Mapm algorithm. It’s a multi-attributes correlated pattern mining algorithm, based on the correlation operation of multiple attributes, through the results of mining to find similar users, so as to achieve the forecast purpose of real-time power consumption.

Keywords

Energy internet Frequent attribute pattern Power attribute correlation 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61701104), by SRF for ROCS, SEM and by the Science Research of Education Department of Jilin Province (No. 201698).

References

  1. 1.
    Kamyab, F., Amini, M., Sheykhha, S., Hasanpour, M., Jalali, M.M.: Demand response program in smart grid using supply function bidding mechanism. IEEE Trans. Smart Grid 7(3), 1277–1284 (2016)CrossRefGoogle Scholar
  2. 2.
    Bulkeley, H., McGuirk, P.M., Dowling, R.: Making a smart city for the smart grid? The urban material politics of actualising smart electricity networks. Environ. Plan. A 48(9), 1709–1726 (2016)CrossRefGoogle Scholar
  3. 3.
    Maharjan, S., Zhu, Q., Zhang, Y., Gjessing, S., Başar, T.: Demand response management in the smart grid in a large population regime. IEEE Trans. Smart Grid 7(1), 189–199 (2016)CrossRefGoogle Scholar
  4. 4.
    Tajer, A., Dobson, I., Kar, S., Lavaei, J., Xie, L.: Guest editorial the theory of complex systems with applications to smart grid operations. IEEE Trans. Smart Grid 7(4), 1949–1950 (2016)CrossRefGoogle Scholar
  5. 5.
    Gong, Y., Cai, Y., Guo, Y., Fang, Y.: A privacy-preserving scheme for incentive-based demand response in the smart grid. IEEE Trans. Smart Grid 7(3), 1304–1313 (2016)CrossRefGoogle Scholar
  6. 6.
    Boustani, A., Maiti, A., Jazi, S.Y., Jadliwala, M., Namboodiri, V.: Seer grid: privacy and utility implications of two-level load prediction in smart grids. IEEE Trans. Parallel Distrib. Syst. 28(2), 546–557 (2017)Google Scholar
  7. 7.
    Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst. Appl. 41(13), 6047–6056 (2017)CrossRefGoogle Scholar
  8. 8.
    Sideratos, G., Hatziargyriou, N.D.: An advanced statistical method for wind power forecasting. IEEE Trans. Power Syst. 22(1), 258–265 (2007)CrossRefGoogle Scholar
  9. 9.
    Frank, A.G., Ribeiro, J.L.D., Echeveste, M.E.: Factors influencing knowledge transfer between NPD teams: a taxonomic analysis based on a sociotechnical approach. R&D Manag. 45(1), 1–22 (2015)CrossRefGoogle Scholar
  10. 10.
    Han, H., Wu, X.L., Qiao, J.F.: Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm. IEEE Trans. Cybern. 44(4), 554–564 (2014)CrossRefGoogle Scholar
  11. 11.
    Chen, S.M., Manalu, G.M.T., Pan, J.S.: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization technique. IEEE Trans. Cybern. 43(3), 1102–1117 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., Niu, D., Ji, L.: Short-term power load forecasting based on IVL-BP neural network technology. Syst. Eng. Procedia 4, 168–174 (2012)CrossRefGoogle Scholar
  13. 13.
    Zhang, X., Pan, F., Wang, W., Nobel, A.: Mining non-redundant high order correlations in binary data. In: VLDB Endowment, pp. 1178–1188 (2012)CrossRefGoogle Scholar
  14. 14.
    Karunaratne, T.M.: Learning predictive models from graph data using pattern mining. Doctoral dissertation, Department of Computer and Systems Sciences, Stockholm University (2014)Google Scholar
  15. 15.
    Hong, T.P., Wu, J.M.T., Li, Y.K., Chen, C.H.: Generalizing concept-drift patterns for fuzzy association rules. J. Netw. Intell. 3(2), 126–137 (2018)Google Scholar
  16. 16.
    Philippe, F.V., Jerry, C.W.L., Rage, U.K., Yun, S.K., Rincy, T.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)Google Scholar
  17. 17.
    Jiang, C., Coenen, F., Zito, M.: A survey of frequent subgraph mining algorithms. Knowl. Eng. Rev. 28(1), 75–105 (2017)CrossRefGoogle Scholar
  18. 18.
    Singh, V.K., Shah, V., Jain, Y.K., Shukla, A., Thoke, A.S., Singh, V.K., Parganiha, V.: Proposing an efficient method for frequent pattern mining. World Acad. Sci., Eng. Technol. 61(48), 384–390 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, School of Computer ScienceNortheast Electric Power UniversityJilinChina

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