Predicting the performance of hybrid ventilation in buildings using a multivariate attention-based biLSTM Encoder-Decoder neural network
This provides a more efficient control-oriented model for building energy management, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of predicting indoor air temperature in buildings with hybrid ventilation by developing a multivariate attention-based biLSTM encoder-decoder neural network, achieving accurate predictions for five zones with errors plateauing after 24 steps (6 hours ahead).
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).