Engineering-based generic modeling of occupant behavior for energy efficient buildings

这个项目的目的是开发一种全面的方法来模拟建筑中与能源消耗相关的居住者行为(OB)。由于OB对建筑能耗的影响很大，基于最新的研究现状，本项目申请中提出的方法将在手动可操作窗的子案例上实施。所获得的知识随后将用于对建筑物中进一步的与能源相关的用户行为进行建模。为此，OB动作被定义为离散时间序列。将特别注意这些模型的推广，以使其适用于各种建筑物的居住者。个性化和泛化能力被视为至关重要的组成部分。这种通用的OB建模方法目前在建筑性能模拟和楼宇自动化系统(BAS)中都是一个重要的缺失组件，因为在能源转型和智能电网控制的分散能源系统的背景下，OB模型的可靠性和适应性变得越来越重要。更具体地说，将研究不同结构的前馈神经网络和递归神经网络(RNN)的适用性。在第一个研究任务中，前馈神经网络被用于基于物理传感器测量并结合表示时间序列的概率图形模型来预测用户行为。第二个案例包括发展递归神经网络(RNN)来预测人类的行动。这包括研究最佳网络架构以及门控结构的适宜性，包括用于基于物理系统和执行的操作的先前更改的时间序列对操作进行建模的长短期内存(LSTM)。

The aim of this project is to develop a comprehensive method for modeling energy consumption-related occupant behavior (OB) in buildings. Due to the high impact of OB on a building’s energy consumption and based on the current state of the art research the methods proposed in this project application shall be implemented on a subcase of manually operable windows. The resulting knowledge will be subsequently used to model the further energy-related user actions in buildings. For that purpose, OB actions are defined as discrete time sequences. Special attention will be put on the generalization of the models to enable its applicability for a wide range of building occupants. Individualization and generalization capabilities are regarded as crucial components. Such a generic OB modeling approach is currently an important missing component in both, building performance simulation and building automation systems (BAS), since reliability and adaptability of OB-models become increasingly relevant in the context of the energy transition and smart grid controlled, decentralized energy systems.The modeling methods belong to the category of deep learning. More specifically, different architectures of feed forward and recurrent neural networks (RNN) will be researched with respect to their applicability. In the first research task, a feed forward neural network is used to predict user actions based on physical sensor measurements in conjunction with a probabilistic graphical model that represents temporal sequences. The second case includes the development of recurrent neural networks (RNN) to predict human actions. This includes research on the optimal network architecture as well as suitability of gated structures, including long short-term memory (LSTM) used to model actions based on the time-series of previous changes in physical systems and performed actions.