Multiagent System | KB and Ontology | Control Strategies | Simulation Environment
· Multiagent System
The intelligence part of the ThinkHome system is implemented as a multiagent system (MAS). MAS is a powerful logical methodology that perfectly fullfill the requirements of complex scenarios from a top-down approach, mainly in terms of distributed intelligence, for providing encapsulation on a functional level and for natively supporting communication among different system parts. In addition, the use of the agent paradigm also brings along independent evolution, exchange, and maintenance of the autonomous parts that are implemented as agents. The use of well-deﬁned interfaces helps to retain the required autonomy and even permits a possible local distribution of components.
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· Knowledge Base and Ontology
The task of the knowledge base is to intelligently maintain all relevant concepts that are considered to be inﬂuence factors in a smart home. Thus, it stores details on users like their preferences and proﬁles, current occupancy and activities (i.e., context), as well as schedules. Likewise, also weather data and building conditions are conceptualized mainly to enable dynamic optimizations. Furthermore, the KB keeps information about the building: it integrates data already collected during the architectural conception and construction process of a building, in particular comprising data on the building structure, building orientation, used materials, and related properties of these items. It also stores information on all resources (e.g., devices) that are available within the smart home, including energy-related aspects. Viewed in a global context, the KB is the foundation for the MAS and basically supports the system to infer the most appropriate building control strategies, that is, those that are most energy effcient and comfort oriented in the current situation. Additionally, the KB functions as an abstraction layer of the underlying Building Automation System (BAS). As it is not relevant for control strategies to be aware of the concrete installations in the building, but rather of the services they other, the KB provides a generic and integrated view of the different devices, networks and related functionalities to the higher system part. Taken together, this part of the system represents the shared vocabulary used by the MAS for execution of advanced control strategies. It is therefore fundamental in grounding ThinkHome.
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· Control Strategies
The control strategies supports the basis of the intelligent operation of a ThinkHome building. They are responsible for the calculation of the actions (switching commands, start/stop times and many other parameters) that are executed by the underlying BAS. The control strategies are implemented in a dedicated agent. Hence, they are embedded in the agent framework (MAS) and can access all information that is available in the system, either directly, by communication, or even by cooperation with other agents.
The control strategies are built over artificial intelligence methods and algorithms in order to acquire pervasive and context aware decision capabilities. In short, control strategies face the resolution of specific "uses cases" and applications taking advantage of the structure, resources and framework established by the MAS and KB, and also submitted to the supervision of the competent MAS agents.
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· Simulation Environment
Due to the difficulties to evaluate experimental approaches in the built environment, simulation is a suitable means for designing energy efficient buildings and also for checking control strategies, the application of artificial intelligence in BAS solutions and the performance optimization of holistic concepts (like ThinkHome) as a whole.
In addition, simulation is not only a tool useful in design or research phases, but a method available for advanced holistic systems to make predictions and obtain foundations for the next decision making.