  This paper describes a benchmark consisting of a set of synthetic
measurements relative to an office environment simulated with the software
IDA-ICE. The simulated environment reproduces a laboratory at the KTH-EES Smart
Building, equipped with a building management system. The data set contains
records collected over a period of several days. The signals to CO$_2$
concentration, mechanical ventilation airflows, air infiltrations and
occupancy. Information on door and window opening is also available. This
benchmark is intended for testing data-based modeling techniques. The ultimate
goal is the development of models to improve the forecast and control of
environmental variables. Among the numerous challenges related to this
framework, we point out the problem of occupancy estimation using information
on CO$_2$ concentration. This can be seen as a blind identification problem.
For benchmarking purposes, we present two different identification approaches:
a baseline overparametrization method and a kernel-based method.
