The German Federal Government put its first net zero office building in operation in 2013 anticipating EU requirements for all public administrations to solely built "nearly zero energy buildings" from 2019 onwards. The two-storey building is situated in Berlin and has been designed for having an overall annual energy demand lower than the in-situ conversion of renewable energies. The paper shows the results of the first year of the operation. The overall annual electric usage is near to the expected value. Nevertheless, intensive monitoring showed differences between the expected and the measured electric usage in the areas of heating and domestic hot water (+172%), ventilation (-36%), lighting (-33%), equipment and auxiliaries (-14% and -13%, respectively), yield of the PV-system (+32%). A numerical model was created to investigate these deviations as well as to evaluate the impact of possible actions aimed at further improving the energy demand of the building. The model was calibrated with measured data, data from surveys, occupancy schedules, and validated by means of different indexes, among which the mean bias error (MBE). The MBE between monitored and simulated energy performance is below +/- 2% for all stated areas of energy usage. Analogously, the energy needs for cooling, monitored and simulated, as well as the trends of the indoor air temperature in both heating and cooling seasons, are in a very good agreement. In detail, the monthly MBE for the indoor temperature, in specific summer and winter periods and in the considered rooms, is below the threshold of +/- 5%, with values slightly higher (anyway lower than +/- 6%) only at the ground floor. Finally, the paper shows that the reasons for the detected deviations in electric energy usage can mainly be related to the usage pattern of the building and to the occupants' behavior. In order to ensure energy efficiency as high as planned, a suitable monitoring of the energy performance is fundamental. It enables the calibration and validation of tailored energy models to detect possible malfunctions as well as propose actions aimed at further reducing the energy usage of the building. (C) 2016 Elsevier B.V. All rights reserved.