  ML-enabled systems that are deployed in a production environment typically
suffer from decaying model prediction quality through concept drift, i.e., a
gradual change in the statistical characteristics of a certain real-world
domain. To combat this, a simple solution is to periodically retrain ML models,
which unfortunately can consume a lot of energy. One recommended tactic to
improve energy efficiency is therefore to systematically monitor the level of
concept drift and only retrain when it becomes unavoidable. Different methods
are available to do this, but we know very little about their concrete impact
on the tradeoff between accuracy and energy efficiency, as these methods also
consume energy themselves.
  To address this, we therefore conducted a controlled experiment to study the
accuracy vs. energy efficiency tradeoff of seven common methods for concept
drift detection. We used five synthetic datasets, each in a version with abrupt
and one with gradual drift, and trained six different ML models as base
classifiers. Based on a full factorial design, we tested 420 combinations (7
drift detectors * 5 datasets * 2 types of drift * 6 base classifiers) and
compared energy consumption and drift detection accuracy.
  Our results indicate that there are three types of detectors: a) detectors
that sacrifice energy efficiency for detection accuracy (KSWIN), b) balanced
detectors that consume low to medium energy with good accuracy (HDDM_W, ADWIN),
and c) detectors that consume very little energy but are unusable in practice
due to very poor accuracy (HDDM_A, PageHinkley, DDM, EDDM). By providing rich
evidence for this energy efficiency tactic, our findings support ML
practitioners in choosing the best suited method of concept drift detection for
their ML-enabled systems.
