Hybrid ventilation (coupling natural and mechanical 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 model is required. It should enable near-real time forecast of the indoor air temperature and humidity based on operational conditions such as window opening and HVAC schedules. However, widely used physics-based simulation 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 multivariate multi-head attention-based long short-term memory (LSTM) encoder-decoder neural network to predict indoor air conditions of a building equipped with hybrid ventilation. The deep neural network used for this study aims to predict indoor air temperature dynamics when a window is opened and closed, respectively. Training and test data were generated from detailed multi-zone office building model (EnergyPlus). The deep neural network is able to accurately predict indoor air temperature of five zones whenever a window was opened and closed.

翻译：混合通风（自然通风与机械通风耦合）凭借其可靠的控制系统，成为多数气候条件下提供新风的高能效解决方案。为优化此类系统的运行，需建立高保真控制导向模型，该模型应能根据窗户启闭状态及暖通空调日程等运行条件，实现室内空气温度与湿度的近实时预测。然而，广泛应用基于物理的仿真模型（即白箱模型）存在劳动强度高、计算成本大的问题。作为替代方案，基于人工神经网络的黑箱模型可通过训练成为建筑动态特性的优质估算器。本文探究了基于多变量多头注意力机制的长短时记忆（LSTM）编码器-解码器神经网络在预测配备混合通风系统建筑室内空气状态方面的能力。本研究采用的深度神经网络旨在分别预测窗户开启与关闭时的室内空气温度动态变化。训练与测试数据源自详细的多区域办公建筑模型（EnergyPlus）。该深度神经网络能在窗户开闭状态下，精准预测五个分区的室内空气温度。