Smart Buildings Are Built of Smart Data: Knowledge Graphs for Building Automation Systems
“Buildings that almost think for themselves” is how a 1984 NYT article bravely called intelligent buildings as if looking into the future of the building industry and more specifically of the building automation sector.
And they were right. The increasing demand for sustainable designs, efficient management, commercial buildings energy savings and flexibility did direct the building industry towards developing smarter building automation systems (BAS). This in turn led to better occupants and management experiences while saving costs and energy.
Today, intelligent buildings like Oakland City Center collect temperature and humidity data, evaluate it and control the heating, ventilation and air conditioning (HVAC) system for cost-efficient and sustainable energy use. There is also Frasers Tower in Singapore, whose 179 Bluetooth Beacons and 900 lighting, air quality and temperature sensors gather data to enable maximum efficiency of physical space resources and optimal productivity for the occupants. A factory floor in Dresden, for instance, was filled in with sensors for personnel, machine movement and is now helping in optimizing production, keeping up with safety regulations while at the same time ensures continued maintenance of the equipment.
All these soaring numbers of smart buildings and controllers across physical spaces and the vast amounts of data that feed them challenge the building industry. It needs to find sustainable ways to manage physical spaces and the data they consume and produce. And, more importantly, to improve and extend the way data is connected and further managed.
Let’s face it, when you have to automate a building with more than 10 thousand connected devices, which stream and exchange data, you need to find a way to integrate this data smoothly and then manage it efficiently..
Knowledge graphs built with semantic technology have paved the way to seamless integration and efficient future-proof data management.
Sensors, controllers, actuators and other devices all exchange data at unprecedented levels. Left without context, this data cannot become actionable information.
Take for example the most common use of automation functions – lighting control. Reportedly, it is one of the easiest to start with when saving on energy costs. But to take the best advantage of this subsystem, one must optimize the way the data it produces is handled and contextualized.
Case in point, the lighting in an office space can be efficiently automated and scheduled by integrating data coming not only from the light controlling devices but also from photosensors, occupancy sensors, etc. In such a scenario, when the optimal light for each room is automatically turned on, it takes into account all relevant data such as the levels of light coming from outside, the occupancy data, etc. Such optimization is only possible when all this data is properly modeled, described and semantically integrated.
The more formalized, standardized and properly integrated the data is, the more efficient it is to monitor, control and manage the systems that generate it.
Knowledge graph technologies, which have been on the rise and maturing as an enterprise solution for the last few years, allow seamless data integration and easy data management. They also offer the means for meeting the current challenges while tapping into the potential of future opportunities for even smarter building automation systems.
