  The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board
equipment within its pre-defined operating temperatures range. To plan and
optimize the scientific operations of MEX, its operators need to estimate in
advance, as accurately as possible, the power consumption of the thermal
subsystem. The remaining power can then be allocated for scientific purposes.
We present a machine learning pipeline for efficiently constructing accurate
predictive models for predicting the power of the thermal subsystem on board
MEX. In particular, we employ state-of-the-art feature engineering approaches
for transforming raw telemetry data, in turn used for constructing accurate
models with different state-of-the-art machine learning methods. We show that
the proposed pipeline considerably improve our previous (competition-winning)
work in terms of time efficiency and predictive performance. Moreover, while
achieving superior predictive performance, the constructed models also provide
important insight into the spacecraft's behavior, allowing for further analyses
and optimal planning of MEX's operation.
