Gerhard Zucker

Communication and Computation in Buildings: A Short Introduction and Overview

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.

Hierarchical Semantic Processing Architecture for Smart Sensors in Surveillance Networks

Data acquisition by multidomain data acquisition provides means for environment perception usable for detecting unusual and possibly dangerous situations. When being automated, this approach can simplify surveillance tasks required in, for example, airports or other security sensitive infrastructures. This paper describes a novel architecture for surveillance networks based on combining multimodal sensor information. Compared to previous methodologies using only video information, the proposed approach also uses audio data thus increasing its ability to obtain valuable information about the sensed environment. A hierarchical processing architecture for observation and surveillance systems is proposed, which recognizes a set of predefined behaviors and learns about normal behaviors. Deviations from “normality” are reported in a way understandable even for staff without special training. The processing architecture, including the physical sensor nodes, is called smart embedded network of sensing entities (SENSE).

Autonomous Perception and Decision Making in Building Automation

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 todays 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.

Psychoanalytical model for automation and robotics

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.

Model based predictive control for a solar-thermal system

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.

Current developments and challenges in building automation

The classical systems of building automation systems (BAS) have evolved from control of heating, ventilation and air-conditioning (HVAC). The wide use of fieldbus technology and powerful embedded systems has enabled new developments. Building automation is employed to integrate user requirements, system requirements and optimizations in order to maintain user comfort—with energy efficiency being a recently added optimization goal. The classic three-layer automation model is transferred into a service-oriented architecture (SOA) of objects. At the same time, the complexity increases as new services are added. Object-oriented solutions are an approach to cope with this. The basic design of controllers has become distributed and is adopting advanced methods. The resulting, highly integrated systems require a defense-in-depth strategy to ensure security. We take a look at the building services today and in the near future, highlight the strength of integrated building automation over different domains and industries and show the upcoming challenges for building automation systems.ZusammenfassungDie klassischen Systeme der Gebäudeautomation stammen aus dem Bereich Heizung, Lüftung, Klima (HLK). Der Einsatz von Feldbustechnologien, IP-basierten Netzen und leistungsfähigen Embedded-Systemen ist heute Stand der Technik. Die Gebäudeautomation deckt immer mehr Gebäudedienste ab, und durch gewerkeübergreifende Systeme werden neue Funktionen möglich. Durch vertikale Integration wird die bisherige Hierarchie der Automationspyramide weitgehend aufgelöst und in ein flaches, verteiltes System offener Protokolle und Funktionen überführt. Neben typischen Zielen, die Betriebseffizienz eines Gebäudes und den Komfort zu erhöhen, verlangen aktuelle Richtlinien darüber hinaus weitgehende Möglichkeiten zur Aufzeichnung, Protokollierung und Eingriffnahme für die Energieoptimierung in bestehenden und neuen Systemen. Die resultierenden, komplexen Systeme verlangen nach effizienten Methoden des Engineering. Die geforderte Offenheit legt außerdem wachsende Anforderungen an Sicherheitskriterien zu Grunde.

Demand response with functional buildings using simplified process models

Smart Grids ideally interconnect intelligent grid members. One big share of grid presence is with buildings. Flexible and grid-friendly buildings would improve grid management and are an important contribution to the integration of renewable energy sources. Classical buildings, however, are passive and not cooperative. This article describes how electro-thermal processes in buildings can be used for demand response and how such intelligent behavior can be enabled via communication technology. Experiments and simulations on typical mid-European buildings were done to estimate the potential time constants.

Building energy management and data analytics

Energy efficiency in buildings depends on the way the building is operated. Therefore energy management is the key component for efficient operation. Data analysis of operation data helps to better understand the systems and detect faults and inefficiencies. The facility manager benefits from smart analysis that makes use of machine learning algorithms and innovative visualizations. This analysis is part of a bigger review of the current structure of building automation as it is used in todays buildings. The operation targets in energy efficiency are complex, ambiguous and contradictory: indoor comfort, energy efficiency, high availability and low costs cannot be met at the same time. In order to improve building operation, a novel model of automation is discussed. The foundation of this model is in cognitive automation, since each building is unique in its selection of energy sources, architecture, usage and location, which implies that the buildings control system has to be adapted individually. This paper connects the data-driven analysis of operation data with a cognitive concept to be used for operating the energy systems in a building and regarding goals on how to optimally operate while considering constraints about the limits of operation, using the complex, dynamic data from building automation.

New approach for controlling complex processes. An introduction to the 5 th generation of AI

In automation mechanical units are more and more replaced by electronic components. They contain sensors, actuators and information computing systems. The goal is to achieve higher process and product quality. That means, for example, to increase the performance by implementing more functionality. Systems become interconnected, which causes increased complexity - something that cannot easily be handled with traditional technological approaches. It is necessary to rethink existing approaches and find new solutions. These could be found in bionic models. Contemporary researchers like Mark Solms and Antonio R. Damasio build upon psychoanalysis, which could be such a new approach for engineers. The theories of psychoanalysis are widely unknown to engineers, still their methods, which originate from neurology, are partly similar to the methods of computer technology. It is merely the theoretical foundation of psychoanalysis, which is complex and at the start hard to grasp for an engineer. Cooperation between engineers and psychoanalysts thus has to overcome this hurdle before being successful. But if we want to conduct our scientific work consequently, it is high time that we get acquainted with psychoanalysis. The following paper wants to show how to install such cooperation and tries to outline the idea of a model that results from already existing collaboration.

Improving energy efficiency of buildings using data mining technologies

Building automation systems record operation data including physical values, system states and operation conditions. This data is stored, but commonly not automatically evaluated. This historic data is the key to efficient operation and to quick recognition of errors and inefficiencies, a potential that is not exploited today. Instead, today the evaluation during operation delivers only alarming in case of system failures. Analysis is commonly done by the facility manager, who uses his experience to interpret data. Methods from data mining and data analysis can contribute to a better understanding of building operation and provide the necessary information to optimize operation, especially in the area of Heating, Ventilation and Air Conditioning (HVAC) systems. Increases in energy efficiency and can be achieved by automated data analysis and by presenting the user energy performance indicators of all relevant HVAC components. The authors take a first step to examine operation data of adsorption chillers using the X-Means algorithm to automate the detection of system states.