  A key science goal of upcoming dark energy surveys is to seek time evolution
of the dark energy. This problem is one of {\em model selection}, where the aim
is to differentiate between cosmological models with different numbers of
parameters. However, the power of these surveys is traditionally assessed by
estimating their ability to constrain parameters, which is a different
statistical problem. In this paper we use Bayesian model selection techniques,
specifically forecasting of the Bayes factors, to compare the abilities of
different proposed surveys in discovering dark energy evolution. We consider
six experiments -- supernova luminosity measurements by the Supernova Legacy
Survey, SNAP, JEDI, and ALPACA, and baryon acoustic oscillation measurements by
WFMOS and JEDI -- and use Bayes factor plots to compare their statistical
constraining power. The concept of Bayes factor forecasting has much broader
applicability than dark energy surveys.
