Abstract-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.

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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 CO 2-driven control. INDEX TERMS Automated control strategy development, building information modeling (BIM), controller optimization, HVAC control, industry foundation classes (IFC).

Buildings and their components account for a major amount of the overall global energy consumption. There is a rising demand to increase the end-use energy efficiency. Advanced automation and control for buildings and their components is one possibility how to achieve the desired goal of lower energy consumption. The model based predictive control approach as a special form of optimal control offers a good way to increase energy efficiency. This paper presents the employment of a model based predictive control algorithm for the energy efficient temperature control of a solar-thermal system consisting of a solar collector and a heat exchanger. The design of the controller is based upon a physical lumped model of the system components. In order to illustrate the potential of the model predictive approach for the use in building automation the comparison to a standard PI control approach is made where the energy consumption for both control concepts is analyzed.

Building Information Modeling (BIM) data are typically exchanged using the Industrial Foundation Classes (IFC) standard. An IFC-based BIM model is a container for data that is created during the design and planning phase and is therefore a rich source of information for the commissioning phase, in which building services are brought to operation. This paper examines the use of BIM data for automated generation of control strategies for energy systems, thus simplifying and accelerating the commissioning phase. We present a methodology to create control strategies of a building heating system with several variations of renewable energy systems and include both heat provisioning and a distribution system. The control goals include favoring the use of non-fossil energy, which is provided by a combination of photovoltaic system (PV), heat pump (HP) and industrial excess-heat source. Thermal energy storages are integrated for load shifting purposes and the control of the heat distribution system is designed towards the requirements of building physics, occupancy and outside climate conditions. A validation of the approach is presented in a combined SIMULINK and TRNSYS simulation environment.

Research in automation focuses on systems which are capable of solving very complex tasks and problems. Artificial Intelligence and especially Cognitive Science have brought remarkable successes; however, in some areas the boarders of feasibility and further extension are reached. Compared to human intelligence the range of capabilities of the solutions is still modest. In the following we will argue why we see the necessity to introduce a novel approach for creating models, which possibilities and tools computer engineering can offer, why a psychoanalytical template is considered meaningful, and which open problems could be tackled or even broken through with this approach, respectively. The article is based on comprehensive research results in the course of several research projects including a European one. Involved persons originate from a number of research institutions in Austria, South Africa, and Canada.

La transferencia de tecnología sostenible es compleja para las firmas de construcción. Una posible solución es analizar esa clase de transferencia como una red social ya que, si se identifican las diferentes relaciones entre los actores del sector construcción, es posible evaluar la capacidad de adaptación tecnológica de dichos actores. El objetivo fue evaluar la transferencia de tecnología sostenible entre empresas constructoras internacionales que se dedican a construir vivienda social o accesible. Para esto, se identificaron dos países con capacidad de transferencia de tecnología sostenible (Reino Unido y Estados Unidos) y dos países de menor capacidad tecnológica y con potencial de adaptarse a dichas tecnologías (Brasil y Colombia); posteriormente, se seleccionaron cinco firmas constructoras por cada país, con las cuales se hizo un análisis de redes (brasilbragrado, intensidad, cercanía y densidad), y luego, procesos de simulación. Como resultado se identificó la capacidad de transferencia tecnológica que tienen las empresas latinoamericanas para aceptar y adaptar tecnologías de empresas de países industrializados, y se espera poder desarrollar indicadores de medición de transferencia tecnológica que permitan comprender mejor la complejidad de la vivienda social.