4 pillars for transparent, digital buildings

In order to digitally evaluate the data generated by building systems, the devices of domain-specific systems must be reliably linked to the cloud. Data generated in a device first flows via a secure connection to edge and/or IoT gateways. There it is prefiltered and/or aggregated so that only relevant data continues on to the cloud. The point of this is to pare the overall data volume to manageable levels. The gateway consolidates all protocols so that data from equipment of different makes can be analyzed together, and then passes the resulting pre-processed data to the cloud where it is continuously and thoroughly evaluated to derive appropriate recommendations for action by users. The gateways also monitor data flowing in the opposite direction to keep all IT systems and other equipment safe and secure.

To obtain a complete, faithful image of a building, the described physical operational and maintenance view is transformed into a digital counterpart in real time. The resulting “digital building twin” receives a constant stream of data from the building’s systems and monitors, which it analyzes using a defined system process that emulates the corresponding real-world context. It highlights interconnections among individual systems and enables early-stage detection of any inefficiencies or possible malfunctions. Interpretation of the data is facilitated by considering it in the context of a particular building or room. For example, an indoor temperature of 23°C can mean different things for a server room and an office. The digital building twin is based on semantic models that describe a building and the interconnections among its systems in encyclopedic detail.

Ongoing capture and evaluation of data in a particular context also makes it possible to view system performance over time. Semantically qualified time series are analyzed to reveal anomalies and rapidly detect and predict malfunctions. The ability to almost instantaneously take action when required considerably reduces maintenance-related work and expenditures. It eliminates unnecessary work on the one hand while enabling rapid responses when required on the other, thus preventing incipient problems from snowballing through downstream processes and possibly causing major breakdowns. Semantic prequalification and assessment in the context of a building or room instantly reveals exactly where steps need to be taken. “Predictive maintenance” thus makes it easier to effectively plan maintenance work.

On the basis of existing time series, a building twin can be used to simulate scenarios and generate recommendations for efficient and comfortable building operation. It can reveal, for example, how much energy could be saved under certain conditions (for example, if the heating and lighting were turned off in rooms and/or on floors that aren’t in use) or identify the optimal indoor temperature (such as by linking booking and heating systems). It’s up to the users and operators in each case to decide whether the corresponding actions should be partly or completely automated. Even remote maintenance with “augmented reality” becomes possible when service technicians can access all required data and perform maintenance tasks remotely. The importance of external data, such as weather forecasts or information from outside systems, is also growing for identifying the parameters for optimal building operation.