Die Raumautomation (RA) umfasst die automatisierten Verschattungs-, Beleuchtungs- und Klimatisierungssysteme auf der Raumebene eines Gebäudes und ist ein bedeutender Einflussfaktor bei der Nachhaltigkeit von Gebäuden. Der Einfluss der RA auf die ökologischen, ökonomischen und sozialen Aspekte der Nachhaltigkeit eines Gebäudes sind Teil zahlreicher verschiedener Forschungsarbeiten. Allerdings wurde die Nachhaltigkeit der RA bei dem Entwurfsprozess der RA sowie bei der Anpassung und der Nachrüstung der RA selten bzw. nicht ausreichend erforscht. Für diese Forschungslücke wird in dieser Arbeit der Begriff funktionale Nachhaltigkeit des Engineerings und Reengineerings der RA sowie des resultierenden RA-Systems vorgestellt. Darunter werden die effiziente Einsetzbarkeit bzw. Wiederverwendbarkeit von RA-Geräten bei Szenarien des Engineerings und Reengineerings sowie die Wiederverwendbarkeit von aktuellen Engineeringdaten in verschiedenen Engineeringaufgaben im Laufe des Lebenszyklus eines Raumautomationssystems (RAS) verstanden. Ziel der vorliegenden Arbeit ist die Entwicklung eines Konzepts für eine flexible Roadmap inkl. der dazu benötigten Datenstruktur zur Sicherstellung und Verbesserung der funktionalen Nachhaltigkeit für Engineering und Reengineeringprozesse der RA sowie der daraus resultierenden RA-Systeme. Diese Roadmap kann als Teil einer kontinuierlichen Performance-Evaluation eingesetzt werden. Der Einsatz dieses Lösungsansatzes wird mit unterschiedlichen praxisrelevanten Ansatzpunkten (Szenarien) entlang des Lebenszyklus eines Raumautomationssystems geprüft. Als Baustein der Roadmap wird die Einsetzbarkeit existierender Technologien für den RA-Entwurf untersucht und die benötigten Erweiterungen bzw. die Entwicklung neuer Algorithmen für einen integralen Einsatz vorgestellt. Ein Schwerpunkt der neuen Algorithmen ist die Verbesserung der Anforderungsmodellierung und des Anforderungserfassungsprozesses der RA. Das neue Konzept der Anforderungsvarianten ermöglicht eine bessere Strukturierung der Anforderungen und die Modellierung der semantischen Bedeutung von zusammenhängenden Anforderungen sowie komplexeren Anforderungsaspekten wie Energieeeffizienzklassen (EEK). Verschiedene Algorithmen werden vorgestellt, welche die Modellierung, Ermittlung und Verwaltung von Anforderungsvarianten ermöglichen. Ein weiterer Fokuspunkt liegt in der Entwicklung von Ansätzen zur Unterstützung der Nutzer bei Reengineeringaufgaben. Dazu gehören Ansätze wie die auslöser-abhängige Ermittlung des anzupassenden Bereiches des RA-Systems bei unterschiedlichen Szenarienklassen, die Anpassung der unterschiedlichen Modellebenen bei verschiedenen Szenarien (z.B. zur Verbesserung der EEK) und ein Mechanismus zur Ermittlung des Erweiterungspotentials eines RAS. Die entwickelten Algorithmen, Verfahren und Modelle werden in Form von Unterstützungsfunktionen eines Tools zum automatischen RA-Entwurf prototypisch implementiert, in einen anpassbaren Prozessablauf eingesetzt und begleiten den Nutzer so bei verschiedenen Engineeringund Reengineeringaufgaben. / Room automation (RA) spans across important trades in a building, such as shading, lighting, heating, ventilation, and air conditioning. Although it addresses the room level, RA has a great impact on the sustainability of the building as a whole. The improvement of different aspects (environmental, economic, and social) of buildings' sustainability through the application of RA has been the objective of various research. However, there has been insufficient research regarding the sustainability of the RA itself during its planning, modification, and retrofit. A new concept regarding this research gap can be defined, namely the functional sustainability of the process of engineering and reengineering of RA as well as the resulting RA system. This new aspect of sustainability includes, on the one hand, the efficient deployment and reusability of RA devices during different engineering and reengineering use cases and on the other hand the efficient reusability of engineering data generated during the life cycle of the RA system. This work aims to develop a new concept for a flexible roadmap and the required data structures to support a functional sustainable and user-oriented engineering and reengineering of RA. The advisory component can be applied to different practical use cases during the life cycle of a room automation system. Different algorithmic components are defined as part of the component. The applicability of existing RA design technologies for the development of these algorithmic components is investigated. Based on this, necessary functional extensions of existing approaches or new approaches for the automated design and redesign of RA as part of the roadmap's components are introduced. The first part of the roadmap's algorithmic components aims to improve the quality of the requirement modeling and engineering of RA. A new concept, namely the requirement variant, is introduced. This concept improves the structuring of the requirement set and allows the modeling of semantic meaning behind interrelated requirements as well as complex forms of requirements, such as the energy efficiency class (EEC). Using this concept, various algorithms for the modeling, identification, and management of requirement variants are introduced. A variety of algorithms and courses of action to support the user with different reengineering tasks are introduced. This includes an approach to identify the part of the RA system that has to be modified, the modification of layers of the system model for different use cases (e.g. to improve the system's EEC); and a method to identify extension potential of a RA system. The introduced models, algorithms, and courses of action are implemented prototypically as user-support functions of a tool for the automated design of RA. These components of the roadmap are applied to different use cases through a flexible workflow and provide support to the user during different engineering and reengineering tasks.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 211