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

基于工程的节能建筑居住者行为通用建模

• 批准号: 418297274
• 负责人: Professor Dr.-Ing. Christoph van Treeck
• 金额: --
• 依托单位: Lehrstuhl für Energieeffizientes Bauen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/418297274?language=en
• 关键词: Engineering; based; generic; modeling; occupant

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.

本项目的目的是开发一种综合的方法来模拟建筑物中与能源消耗相关的居住者行为（OB）。由于OB对建筑物能耗的影响很大，根据当前最新研究，本项目申请中提出的方法应在手动操作窗户的子案例上实施。由此产生的知识将随后用于模拟建筑物中与能源相关的用户行为。为此，OB动作被定义为离散时间序列。将特别注意模型的推广，使其适用于广泛的建筑物占用。个性化和泛化能力被视为关键组成部分。这种通用OB建模方法目前是建筑性能仿真和楼宇自动化系统（BAS）中缺少的重要组成部分，因为OB模型的可靠性和适应性在能源转型和智能电网控制的分散能源系统的背景下变得越来越重要。建模方法属于深度学习的范畴。更具体地说，前馈和递归神经网络（RNN）的不同架构将研究其适用性。在第一个研究任务中，前馈神经网络被用来预测用户的行为的基础上，物理传感器测量结合概率图形模型，表示时间序列。第二种情况包括开发递归神经网络（RNN）来预测人类行为。这包括对最佳网络架构以及门控结构的适用性的研究，包括用于基于物理系统和执行动作的先前变化的时间序列对动作进行建模的长短期记忆（LSTM）。