  Hybrid ventilation is an energy-efficient solution to provide fresh air for
most climates, given that it has a reliable control system. To operate such
systems optimally, a high-fidelity control-oriented modesl is required. It
should enable near-real time forecast of the indoor air temperature based on
operational conditions such as window opening and HVAC operating schedules.
However, physics-based control-oriented models (i.e., white-box models) are
labour-intensive and computationally expensive. Alternatively, black-box models
based on artificial neural networks can be trained to be good estimators for
building dynamics. This paper investigates the capabilities of a deep neural
network (DNN), which is a multivariate multi-head attention-based long
short-term memory (LSTM) encoder-decoder neural network, to predict indoor air
temperature when windows are opened or closed. Training and test data are
generated from a detailed multi-zone office building model (EnergyPlus).
Pseudo-random signals are used for the indoor air temperature setpoints and
window opening instances. The results indicate that the DNN is able to
accurately predict the indoor air temperature of five zones whenever windows
are opened or closed. The prediction error plateaus after the 24th step ahead
prediction (6 hr ahead prediction).
