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).

翻译：混合通风是一种在大多数气候条件下提供新鲜空气的节能方案，前提是配备可靠的控制系统。为优化此类系统的运行，需要高保真的面向控制模型，该模型应能根据窗户开闭及暖通空调运行时间表等操作条件，实现室内空气温度的近实时预测。然而，基于物理模型的控制导向模型（即白箱模型）存在劳动强度大且计算成本高的问题。相比之下，基于人工神经网络的黑箱模型可通过训练成为建筑动力学的良好估计器。本文研究了一种深度神经网络（DNN）——即基于多元多头注意力机制的长短期记忆（LSTM）编码器-解码器神经网络——在窗户开闭状态下预测室内空气温度的能力。训练与测试数据来源于详细的多区域办公楼模型（EnergyPlus），采用伪随机信号设置室内空气温度设定点和窗户开窗实例。结果表明，该DNN能够准确预测五个区域在窗户开闭状态下的室内空气温度，预测误差在超前24步预测（即超前6小时预测）后趋于稳定。