Abstract
Grey-box models combine a relatively simple physical description of the building with a data-driven inference of key parameters and are often used for this purpose. A challenge with grey-box models is that the model identification process requires 'rich' datasets, meaning datasets containing enough statistical variability on both heating demand and indoor temperatures. Such datasets are scarcely available, usually only from dedicated experiments in living labs or similar research facilities. This study aims to present a series of datasets that can be used for the identification of grey-box models of apartment blocks. Special test periods are simulated in IDA ICE during which representative archetypes of apartment blocks in Norway are excited with trains of heating events, Pseudo-Random Binary Sequence (PRBS), aiming at exploring a wide and rapidly changing set of indoor temperatures within and outside the thermal comfort zone.