iNESS 360 has announced the addition of Data Twins of Commercial Spaces to Bentley Systems’ Powered by iTwin programme. Data Twins of Commercial Spaces enables owners and operators to better manage the use of commercial spaces by integrating human behaviour modelling with a building’s operations.
“Imagine an internet where buildings are connected and talking. In the future, anyone would be able to buy data from commercial buildings. This data is the evolution of how companies are able to interact with modern commercial spaces. INESS is proud to announce our adoption of the Bentley iTwin platform to create the next version of Data Twins for Commercial Spaces that is not only smart, but also intelligent,” said CEO Soner Haci.
INESS converts commercial spaces into data streaming sources to model human behavior and how indoor circumstances affect that behavior by integrating the INESS system into the structure of the building. The system developed predictive models on usage trends and indoor circumstances to optimize space, energy usage, and operations without needing to invest in expensive BMS systems
By using the Bentley iTwin platform INESS will integrate the data and data models directly to the building architectural model and create a data twin of the day-cycle of the building.
“We are very pleased to have iNESS 360 add their Data Twins of Commercial Spaces to the Powered by iTwin programme. In doing so, iNESS 360 joins the growing ecosystem of software developers enabling digital twin solutions with the iTwin platform. Their Data Twins of Commercial Spaces solution is a great example of how digital twin technology can model human behaviour with streaming data to optimize indoor space, energy usage, and operations,” added Adam Klatzkin, Vice President, iTwin Platform, at Bentley Systems.
INESS is a behavioural occupancy analysis system for small-medium-sized commercial properties. INESS converts commercial spaces into data streaming sources to model human behaviour and how indoor circumstances affect that behaviour by integrating the INESS system into the structure of the building. The system developed predictive models on usage trends and indoor circumstances.
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