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 measurement records collected over a period of several days. The signals correspond to CO2 concentration, mechanical ventilation airows, 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 focus on the problem of occupancy estimation using information on CO2 concentration, which we treat as a blind identification problem. For benchmarking purposes, we present two different identification approaches: a baseline overparameterization method and a kernel-based method.