Advertisement

Predicting Thermal Power Consumption of the Mars Express Satellite with Data Stream Mining

  • Bozhidar Stevanoski
  • Dragi Kocev
  • Aljaž Osojnik
  • Ivica Dimitrovski
  • Sašo DžeroskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

Orbiting Mars, the European Space Agency (ESA) operated spacecraft - Mars Express (MEX), provides extraordinary science data for the past 15 years. To continue the great contribution, MEX requires accurate power modeling, mainly to compensate for aging and battery degradation. The only unknown variable in the power budget is the power provided to the autonomous thermal subsystem, which in a challenging environment, keeps all equipment under its operating temperature. In this paper, we address the task of predicting the thermal power consumption (TPC) of MEX on all 33 thermal power lines, having available the stream of its telemetry data. Considering the problem definition, we face the task of multi-target regression, learning from data streams. To analyze such data streams, we use the incremental Structured Output Prediction tree (iSOUP-Tree) and the Adaptive Model Rules from High Speed Data Streams (AMRules) to model the power consumption. The evaluation aims to investigate the potential of the methods for learning from data streams for the task of predicting satellite power consumption and the influence of the time resolution of the measurements of thermal power consumption on the performance of the methods.

Keywords

Data streams Multi-target regression iSOUP-Trees AMRules Satellite Thermal power consumption 

References

  1. 1.
    Aho, T., Ženko, B., Džeroski, S., Elomaa, T.: Multi-target regression with rule ensembles. J. Mach. Learn. Res. 13, 2367–2407 (2012)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Almeida, E., Ferreira, C., Gama, J.: Adaptive model rules from data streams. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part I. LNCS (LNAI), vol. 8188, pp. 480–492. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40988-2_31CrossRefGoogle Scholar
  3. 3.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive Online Analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  4. 4.
    Breskvar, M., et al.: Predicting thermal power consumption of the Mars Express satellite with machine learning. In: 6th International Conference on Space Mission Challenges for Information Technology, pp. 88–93. IEEE (2017)Google Scholar
  5. 5.
    Chicarro, A., Martin, P., Trautner, R.: The Mars express mission: an overview. In: Mars Express: The Scientific Payload, ESA SP 1240, pp. 3–13. European Space Agency, Publications Division (2004)Google Scholar
  6. 6.
    Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44794-6_4CrossRefzbMATHGoogle Scholar
  7. 7.
    De Comité, F., Gilleron, R., Tommasi, M.: Learning multi-label alternating decision trees from texts and data. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 35–49. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-45065-3_4CrossRefzbMATHGoogle Scholar
  8. 8.
    De’Ath, G.: Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 83(4), 1105–1117 (2002)Google Scholar
  9. 9.
    Duarte, J., Gama, J., Bifet, A.: Adaptive model rules from high-speed data streams. ACM Trans. Knowl. Discov. Data 10(3), 30 (2016)CrossRefGoogle Scholar
  10. 10.
    Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13–30 (1963)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ikonomovska, E., Gama, J., Džeroski, S.: Incremental multi-target model trees for data streams. In: ACM Symposium on Applied Computing, pp. 988–993. ACM (2011)Google Scholar
  12. 12.
    Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Discov. 23(1), 128–168 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Khemchandani, R., Chandra, S., et al.: Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 905–910 (2007)CrossRefGoogle Scholar
  14. 14.
    Lucas, L., Boumghar, R.: Machine learning for spacecraft operations support - The Mars Express power challenge. In: International Conference on Space Mission Challenges for Information Technology, pp. 82–87. IEEE (2017)Google Scholar
  15. 15.
    Mitchell, T.: Machine Learning. McGraw Hill, Boston (1997)zbMATHGoogle Scholar
  16. 16.
    Osojnik, A., Panov, P., Džeroski, S.: Tree-based methods for online multi-target regression. J. Intell. Inf. Syst. 50(2), 315–339 (2018)CrossRefGoogle Scholar
  17. 17.
    Pugelj, M., Džeroski, S.: Predicting structured outputs k-Nearest neighbours method. In: Elomaa, T., Hollmén, J., Mannila, H. (eds.) DS 2011. LNCS (LNAI), vol. 6926, pp. 262–276. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24477-3_22CrossRefGoogle Scholar
  18. 18.
    Shi, Z., Wen, Y., Feng, C., Zhao, H.: Drift detection for multi-label data streams based on label grouping and entropy. In: International Conference on Data Mining Workshops, pp. 724–731. IEEE (2014)Google Scholar
  19. 19.
    Spyromitros-Xioufis, E., Spiliopoulou, M., Tsoumakas, G., Vlahavas, I.: Dealing with concept drift and class imbalance in multi-label stream classification. In: 22nd International Joint Conference on Artificial Intelligence, pp. 1583–1588. AAAI (2011)Google Scholar
  20. 20.
    Struyf, J., Džeroski, S.: Constraint based induction of multi-objective regression trees. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 222–233. Springer, Heidelberg (2006).  https://doi.org/10.1007/11733492_13CrossRefGoogle Scholar
  21. 21.
    Vazquez, E., Walter, E.: Multi-output suppport vector regression. IFAC Proc. Vol. 36(16), 1783–1788 (2003)CrossRefGoogle Scholar
  22. 22.
    Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: International Conference on Granular Computing, pp. 718–721. IEEE (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bozhidar Stevanoski
    • 1
  • Dragi Kocev
    • 2
    • 3
    • 4
  • Aljaž Osojnik
    • 2
    • 3
  • Ivica Dimitrovski
    • 1
  • Sašo Džeroski
    • 2
    • 3
    Email author
  1. 1.Faculty of Computer Science and EngineeringSkopjeMacedonia
  2. 2.Jožef Stefan International Postgraduate SchoolLjubljanaSlovenia
  3. 3.Department of Knowledge TechnologiesJožef Stefan InstituteLjubljanaSlovenia
  4. 4.Bias Variance Labs d.o.o.LjubljanaSlovenia

Personalised recommendations