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

 

Project duration:

05/2019 – 10/2021

Project funding:

Deutsche Forschungsgemeinschaft (DFG), 418297274

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 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, 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.