What's the most important type of data for creating smarter buildings?
I'd argue that for every type of building except data centers, it's occupancy data.
Why? Here's a non-conclusive list of reasons:
- It helps building owners (and tenant businesses) understand how their customers are using the space, from office workers to retail customers to school students.
- It helps optimize the Three S's that are vital to controlling building systems and getting to full autonomy: Setpoints, Schedules, and Sequences of Operations.
- It helps solve for the optimal balance between the competing goals of better IAQ and lower energy use.
- It can help improve occupants' experience and find the right space to suit their current needs.
No other type of data can benefit so many building stakeholders.
For good reason, there's been a lot of innovation in this space lately.
- Granularity—out with binary occupancy sensors and in with people counting and real-time location
- Privacy fears—more granularity means more concern for invasion
- Sensor-only vs. Full Stack vs. API-first—the old way was to connect a sensor to the local lighting or BAS controller. New ways bring new architectures and new integration challenges.
- Sensor+analytics teamwork—New types of sensors (body heat, cameras, counters, etc) come packaged with proprietary cloud analytics that crunch the data. Each need the other.
- "Best available" occupancy data—Across a portfolio, occupancy information might come from 15+ different sources. An overlay software or independent data layer is needed to abstract away that complexity.
Nexus Pro members can go deeper with the latest edition of The Lens, which dives right into this topic, including unpacking the trends, recent news, players, and latest fundings. Join Nexus Pro to get access.