Building automation (BA) and smart homes (SHs) have traditionally not been a unified field but varied by their origins, legal foundations, different applications, different goals, and national funding programs for basic research. Only within the last years that an international common focus appeared. The following overview gives not only an introduction into the topic of BA but also the distinction to other areas of automation, in which networks of the field level (the sensor and actuator level) play an important role. Finally, the scientific challenges will be mentioned. SHs are referred to when the differences to BA have to be explicitly stressed. This paper is an introduction for the special IEEE Transactions on Industrial Electronics section on BA and shall introduce the reader to this new topic. BA not only has a huge economic potential but also is of significant academic interest today.

The definition of room functions in Building Information Modeling (BIM) using IfcSpace entities is an important quality requirement that is often not fulfilled. This paper presents a three-step method for enriching open BIM representations based on Industry Foundation Classes (IFC) with room function information (e.g., kitchen, living room, foyer). In the first step, the geometric algorithm for detecting and defining IfcSpace entities and injecting them into IFC models is presented. After deriving the IfcSpaces, a geometric method for calculating the graph of connections between spaces based on accessibility is described; this information is not explicitly stored in IFC models. In the final step, a graph convolution-based neural network using the accessibility graph to classify the IfcSpace entities is described. Local node features are automatically extracted from the geometry and neighboring elements. With the help of a Graph Convolutional Network (GCN), the connection and spatial context information is utilized by the neural network for the classification decision, in addition to the local features of the spaces which are more commonly used. To evaluate the classification accuracy, the model was tested on a set of residential building IFC models. A weighted version of the common GCN was implemented and tested, resulting in a slight improvement in the classification accuracy.

Building Information Modeling (BIM) is a process that collects building data in a central data model. This data can not only be used to plan and construct a building but to design the controls for heating, ventilation and air conditioning (HVAC) systems. The relevant information about the building and its systems is used as the base for controller design, opening new opportunities like the automated testing and optimization of control strategies, both for energy efficiency and user comfort. This paper shows how the information in the BIM is used to design control strategies, including a completeness check and a resulting data set enhancement of this necessary information. It also shows a way, how a building, which is already operating, can be optimized using the operation data from energy systems to modify the existing controllers. The methodology is executed using a ventilation system that provides air quality by means of CO2-driven control.

System complexity has reached a level where it is hard to apply existing information analysis methods to automatically derive appropriate decisions. Building automation is on the verge of being unable to extract relevant information and control a building accordingly. Many different industries in today's automation could provide information by means of different sensors, but the ability to integrate this information is missing. This paper describes approaches on how to cope with increased complexity by introducing models for perception and decision making that are based on findings in neuroscience and psychoanalysis, scientific disciplines that are far-off from engineering but nevertheless promise valuable contributions to intelligent automation.