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)


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.


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


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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

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