.jpg?width=75&name=Image%20(5).jpg)
.jpg?width=75&name=magnus%20andersson_550x550%20(1).jpg)
.jpg?width=75&name=0%20(1).jpg)
-4.png?width=75&name=MicrosoftTeams-image%20(3)-4.png)
The importance of data modelling in commercial buildings has started to move beyond mere space and energy efficiency to the real creation of a living, breathing model of an operational building.
From BMS and BIM to a digital twin?
A building management system, otherwise known as building automation system controls and monitors mechanical and electrical building systems. Building information modelling (BIM) focuses on building design and construction. The digital twin is a real-time virtual representation of an entity, in this case a building.
The BIM software is used to create a collaborative design and build processes, visualising the physical and functional aspects of buildings to enable architects and engineers to build great buildings. BIM has so far not been directly intended for the operation and maintenance of a building, especially not for real-time operational responses.
A digital twin acts as a virtual replica of the physical environment. Enhanced capabilities available for digital twins can enable a truly optimised system, for better indoor climate and less energy usage of the building.
Improving building systems with a digital twin
The digital twin can utilize information and operational technologies such as construction data and building floor plans from BIM, real-time sensor data from the BMS, including data from smart HVAC systems and Internet of Things (IoT) systems, lighting, fire, security and other environmental sensors, as well as data about the building and anonymized data of occupancy and movement in it.
Before and/or during construction, a building digital twin can identify and reduce barriers to installation, selection of providers, cost and maintenance forecasts, construction risks, system interactions and integrations.
At the building system level, a digital twin provides information of how the building theoretically behaves which can be compared to how it actually behaves. This information can help identifying anomalies in the behaviour of the building.
At the device level, the digital twin simulates not only devices, but also the environment around the device, so a digital twin can optimise the indoor climate by predicting and acting on building knowledge and data from others sensors and not only react to data coming from the device itself. For instance, the building can predict the heating load coming through the windows on a southern façade and in advance adjust the cooling accordingly.
Moreover, the digital twin gathers all the necessary data for predictive algorithms, from equipment fault predictions, security risks to building optimisations. Running simulations and experimenting on a digital twin, equipment downtime can be reduced and the building can operate more efficiently.
Making smart buildings even smarter
Smart buildings generate large amounts of data from all building services and the built environment. In buildings with more connected things installed, for example, using IoT devices and the data they generate, the excess of data, systems and devices, are becoming even more complicated. With the desire to provide the best possible indoor climate for the building´s occupants and streamline the costs, the digital twin offers many valuable applications – from the ability to predict maintenance, cost-effectiveness and better operational oversight.
The bigger picture in the future
Over the building´s entire lifecycle, the digital twin will span and evolve to provide a way to simulate construction, respond to changes in occupation or energy supply, the need for building upgrades and assist facility managers in optimising operations.
The trend will be to use digital twins to understand occupants and their needs in their workplaces. As buildings are built with flexibility and occupancy in mind, building information models will also have to evolve to include flexible spaces and people´s behaviour patterns to meet their well-being.
In our case, Swegon will be able to supply data to a digital twin of a building but also use this data with digital twin models of Swegon-supplied machinery.
The systems provided by Swegon stands for a big part of the relevant sensor data for the building, but not all the data to generate a complete model of the building. It is of importance that the customer can take part of this data in an easy way. So rather than solving the whole puzzle we aim at having big pieces that easily fit in by utilizing standard protocols.
