Abstract-In the building automation domain, many prefab ricated devices ... functions leads to a combinatorial explosion of design alternatives at different price ...
Designing Building Automation Systems Using Evolutionary Algorithms with Semi-Directed Variations A. Cernal Oezluek, Joem Ploennigs and Klaus Kabitzsch Institute of Applied Computer Science Dresden University of Technology, Dresden, Germany {cemal.oezluek, joern.ploennigs, klaus.kabitzsch}@inf.tu-dresden.de
Abstract-In the building automation domain, many prefab ricated devices from different manufacturers available in the market realize building automation functions by preprogrammed software components. For given design requirements, the ex istence of a high number of devices that realize the 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 we approach with a multi-objective evolutionary algorithm. By integrating problem specific knowledge into variation operations, a promisingly high optimization performance can be achieved. To realize this, di verse variation operations related to goals are defined upon a classification for the exploration and convergence behavior, and applied in different strategies.
I.
INTRODUCT ION
Building automation systems (BAS) are large networks with distributed intelligence installed in functional buildings such as office buildings, hotels, hospitals, etc. These networks realize different automation functions of various industries such as heating, ventilation, air-conditioning, lighting, shading etc. The functions are realized usually by prefabricated devices which are connected to a field bus. In decentral BAS, the functions are distributed among intelligent components, where the intelligence is supplied by various preprogramroed software modules embedded on each device. Hereby, logical structure is a network of a high number of software modules with different types and complex logical connections. Composition of an optimal logical structure design is a highly complex task, since a very large number of modules embedded on thousands of devices can be required for a design of the whole building. An established approach to handle the high complexity of design task is to break down the BAS design into smaller designs. For the design, a building plan is provided that contains all locations in the building such as storeys, halls, rooms, etc. In a functional building there exist many instances of similar locations, e.g. hundreds of similar office rooms in an office building or similar classrooms at a school. For similar locations, an identical set of BAS functions are required. Thus, from repeated defition of requirements, patterns of requirement sets emerge. Based on this fact, a design is created once for a location type and reused for all locations of the same type. For example, design for an office room type is created
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once and used for all rooms of the same type. Depending on further requirements on individual rooms, minor changes are performed on their designs. As a conclusion, the room automa tion design is the design task with the highest complexity, for which efficient design generation strategies for optimal designs are proposed in this paper. For the design of room automation systems [1], two solution methods currently exist. The first method is creation of designs using specialized tools such as ALEX, LonMaker, ETS and NL220 [2]. Some of these tools provide automated design creation limited to product portfolio of a single manufacturer that is at the same time the owner of the tool. The second and the conventional method supports manufacturer-spanning room automation designs and it is not automated. Designs are created by a system integrator using own expertise to search for interoperable devices that meet the requirements in device catalogs of a few manufacturers which he knows by experience. These two methods can propose almost no trade off solutions; hence, the solutions are suboptimal and high engineering costs must be taken in account. There exist manufacturers, on the one hand, the devices of which meet all given requirements of almost all industries. Such manufacturers use their own design tools developed to work only with own product portfolios. On the other hand, there are manufacturers specialized for certain industries. Costs of designs with the devices of a single manufacturer can be much higher than designs with devices of different manufacturers. However, many devices from different manu facturers are not interoperable! with each other, due to different implementations of software modules. However, there are still interoperable devices from different manufacturers with reasonable prices. With this given optimization potential, an optimization method can be applied for the design creation, that allows all available devices on the market and generates designs with high quality, low price costs. In a previous work [3], we applied a combinatorial optimization method using 1 Devices are interoperable, if and only if they can communicate with each other and the exchanged information is interpreted by each communication partner identically; and can cooperate for the realization of the required functions.
a multi-objective evolutionary algorithm to realize our first approach for generation of room automation designs. The main objective in this paper is to develop variation application strategies with variations that take more advantage of problem-specific knowledge for a better convergence to high quality results. The developed application strategies are: only random, only semi-directed and a combination of the both application strategies. The application strategies are tested for two different complex problem instances and compared to each other for their convergence behavior. In Section 2, we present a related work. In Sections 3 and 4, a description of the problem and the optimization method can be found. In Section 5, we introduce our approach to variation application strategies being the main focus of this paper. In Section 6, test results for two complex examples and a performance comparison are presented. In Section 7, conclusions and future work are presented. II.
RELATED
WORK
The most important criteria for an optimization method are the high quality of results and a fast convergence to optimal solutions. Both criteria can be achieved by a convenient integration of problem-specific knowledge in the operations of the optimization method. In a previous research for a realization of the idea, Weicker et al. [4] used an evolutionary algorithm for optimal positioning of base station transmitters of a mobile phone network and assigning frequencies to the transmitters. The problem was formulated to minimize cost and the channel interface to obtain an optimal quality of service. The authors provided an evolutionary algorithm that integrates multi-objective optimization techniques. Although the application domain is different than the one considered in our approach, the technique used for the integration of problem-specific operations in the multi-objective evolutionary algorithm is similar. The applied solution method concentrates on two phases: In the first phase, the optimal locations for base station transmitters are selected and optimal configurations are found such that the installation area is covered with strong enough radio signals at low deployment costs. In the second phase frequency channels from a fixed frequency spectrum for Global System for Mobile Communications are assigned to cells for a sufficient number of channels assigned to each cell to satisfy all possible simultaneous calls with low interference. The approach minimizes the interference and the costs for transmitters combining both phases. For the handled problem directed and random mutations are applied. Experiments that determine the best proportion between random and directed mutations are performed. The directed mutations identify trans mitters with poor fitting quality and perform replacements or readjust the existing ones by changing parameters such as maximal capacity, power of transmitters etc. Interferences can also be identified to replace the causing frequencies with others. By using this method, a directed mutation can lead to a partial improvement of the individual of variation for the objectives concerned. Tests have been performed to obtain the best proportion among the application rates of random and
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directed mutation and recombination. The design problem of building automation systems is, as a real-world problem, much more complex, since a component based network design problem is handled as a multi-objective combinatorial optimization problem with a logical and a phys ical view containing prefabricated devices with many different software modules. Each module can realize multiple functions depending on the manufacturer's implementation. For each device, different types of software modules are implemented available for varying multiplicities. Moreover, different im plementations of software modules cause the interoperability problem among the devices. There are more criteria related to the validity and quality of designs (cf. Section IV-C), such that the solution method has to deal with a larger variety of trade-off solutions. A directed variation is hard to propose, that can deterministically improve the applied individual for some selected criteria. The logical network design structure is formed upon the given requirements and in most cases, it is not possible to identify design halves to combine best fitting ones into a better developed child. The number and variety of design building device alternatives and software modules depend on the given requirements, hence every different set of requirements can be considered as a new problem instance. In case of applications of suitable directed variations, as applied in [4], the best proportion of random and directed variation rates would vary from a problem instance to another. We follow instead a more generic approach for the generation of designs for different problem instances and apply only semi-directed or semi-directed and random variations in a rotating order with no precondition for proportions. III.
MODEL ING OF DES IGN
A. Design Levels The method for designing building room automation sys tems starts by the elicitation of functional and non-functional requirements and is based on two abstraction levels: Abstract and Detailed Design as illustrated in Fig. 1. The elicitation of requirements is performed by the system integrator in accordance with building automation planners or owners of the building. Based on the requirements, abstract designs are generated for locations (e.g. rooms) in a building [5]. The evolutionary algorithm generates detailed designs and requires abstract designs as input. The specification of an abstract design is function block based and it is independent of manufacturer or a concrete tech nology. The communication relations of the required functions are represented by the abstract connections placed among the ports of function blocks. Function blocks represent the BAS functions that can be classified as sensor, operating, appli cation and actuator functions and can be precisely specified by functional and hardware attributes. The abstract design representation is compliant to the German standard for room automation functions VDI 3813-2 [6], which is designed for function block based and platform and manufacturer indepen dent representations of room automation systems.
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