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Technology  Technik

          Artificial Intelligence in the field of Building


          Automation


          Künstliche Intelligenz in der Gebäude-

          automation





          The term “AI – Artificial Intelligence” is   Similarly, cleaning schedules can be based not  classic building automation with flexible IT-based
          increasingly associated with buildings   only on the current actual values in terms of the  management systems. These offer unrestricted
          and building automation. The question is:   intensity of use of kitchens, canteens and toilets  programming using modern IT languages and
          what is it, where do its tangible benefits   and other areas, but can be based on predictions  tools,  easy  integration  with  other  IT  systems
          lie in this field, and how does the building   drawn from an analysis of usage patterns in the  such as workspace/room reservation systems
          infrastructure need to be adapted to realize   previous days and weeks. This kind of forward-  or data banks, and data visualization for facility
          those benefits?                   looking building management can be applied in  managers and for “ordinary” users.
          Der Begriff „KI – Künstliche Intelligenz“   almost every area of building services, leading to
          wird in zunehmendem Maße mit Gebäu-  increased energy efficiency, reduced operating  The  growing  assimilation of sensor-generated
          den und Gebäudeautomation in Verbindung   costs, improved space utilization and other  data into the IT-based management level opens
          gebracht. Die Frage ist: Was ist künstliche   advantages.            the way for more advanced data processing
          Intelligenz, wo liegen ihre konkreten Vor-                           solutions to come into play – such as AI tools.
          teile für die Gebäudeautomation, und wie   All this – and much more – is possible when  This is the pre-condition for the implementation
          muss die Gebäudeinfrastruktur angepasst   data on building system status and conditions  of any prognosis-based form of building
          werden, um diese Vorteile zu nutzen?  is intelligently evaluated. This requires intensive  management. The sophisticated processing
                                            processing of large amounts of data, with many  of sensor-generated data makes the Smart
          Today’s building automation systems in the  variables to be considered. Artificial Intelligence  Building into a “Cognitive Building”. (Fig. 1)
          main operate ‘statically’ in response to fixed  (AI) offers many new, tailor-made solutions
          time programs or simple control parameters.  which are eminently suited to efficient building  AI-Learning Process
          Room temperature control is based on a preset  management.
          temperature that is the same throughout the day.                     The first step in any Artificial Intelligence process
          Lighting is operated manually, with switches, or  “Building Automation”, “Smart Building”   is system learning. This can take three forms.
          on the basis of simple presence switches. None  and “Cognitive Building”  ƒ  nsupervised Learning
                                                                                 U
                                                                                 S
          of this is truly ‘intelligent’. The new dimension                    ƒ  upervised Learning
                                                                                 R
          that AI can add into the building automation  Initially, “Building Automation” was comparatively  ƒ  einforcement Learning
          environment is to use autonomous analysis  “unintelligent”. Systems were programmed
          of the data as a basis for optimized operation.  to follow a set of simple rules, allowing for  “Unsupervised Learning” is used  when
          Thus, the heating and cooling dynamic of rooms,  quick system start-up and subsequent ease of  large quantities of data must be processed
          weather forecasts, predicted room occupancy  maintenance.            and categorized. This grouping enables the
          during the course of the day can all be factored                     recognition  of  deviations  from  norms  and
          into the operation of the heating.  The “Smart Building” typically builds on this  interdependencies. For example, sensor
                                                                               data from identical circulation pumps can be
                                                                               grouped. If data from one pump or group of
                                                                               pumps deviates from the norm, there may be a
                                                                               defect, and a human engineer can be sent to
                                                                               investigate.

                                                                               “Supervised Learning” often makes use of neural
                                                                               networks. They consist of entry and exit nodes
                                                                               as  well  as  further  nodes  in  the  intermediate
                                                                               layers. Mathematically weighted relationships
                                                                               exist between the diverse nodes (neurons). In
                                                                               order to optimize these relationships, the neural
                                                                               network is subjected to a training phase with
                                                                               known input and output patterns. In the field
                                                                               of building automation, for example, a neural
                                                                               network can “learn” the current consumption
                                                                               profiles of different appliances and which
          Fig. 1: Building Automation – Smart Building – Cognitive Building (source: IBM)
          Abb. 1: Gebäudeautomation – Smart Building – Cognitive Building (Quelle: IBM)  appliances are active when. This information


          38  BACnet Europe Journal 43 09/25
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