bGrid Harnesses the Potential of AI to Deliver People Counting Functionality from Existing bGrid Multi-Sensors
Using machine learning bGrid has managed to generate workplace utilization data from the standard bGrid® multi-sensor data. This will unlock real-time and historic workplace availability insights to all bGrid customers. Also, it is the first step in broader people counting functionalities within the bGrid® solution.
Machine learning plays a vital role in bridging the gap between your occupants needs and your bricks and mortar. The potential of AI in the smart building sector exceeds it’s over usage as a buzzword. By using data inputs from building occupants, which are gathered via multi-sensors, bGrid is mobilizing AI to create accurate data insights now and into the future. Delivering such valuable data driven insights to facility management provides them with actionable insights into what is happening in the building and take care of occupant’s comfort levels. Your physical building may not care about you, but AI makes it possible to create an intuitive smart building that supports and responds to your ever-evolving business needs.
Over the past decade our relationship with the built environment has been influenced with such rapid pace of change in technology; built upon years of steady research and testing to establish the new norm, we quickly get accustomed to smart building features without even noticing them. But advances in technology, design and building materials are not the main driving force for innovation today. People are the most significant influencers here. How we can interact with a building is now more than ever the determining factor to shape a buildings value.
Our bGrid multi-sensors are learning accurate lessons
At bGrid we keep innovating to create purposeful solutions, our team has been successfully experimenting with AI in the development of a data model to forecast and measure workplace utilization with improved accuracy. This workplace model uses machine learning to find correlations between our data and the number of utilized workplaces. In the process of gathering more data our model is continually being trained to teach itself to more accurately estimate workplace utilization.
The challenge in fine tuning the accuracy of the estimated workplace utilization was in overlapping data collection, people walking by in open spaces, individual occupants counted twice, etc. After extensive modelling our bGrid engineers were able to deliver an accurate model that can be used throughout the bGrid sites, but that can also be refined for specific workplace types. The results from comparing the actual value to the estimated workplace utilization reaffirm the confidence that bGrid multi-sensors generate valuable building utilization data with greater accuracy now more than ever.
Image: The bGrid® multi-sensors now deliver workplace utilization data without the need for desk sensors thanks to machine learning and big data from existing bGrid sites.
Our team of experts are always searching for the next great smart building challenge, contact us and tell us what you would like to learn from your building.