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In collaboration with Lawrence Berkeley National Laboratory(Berkeley Lab), Switch Automation has contributed to new research outlining how smart buildings can move from analytics to autonomous operations, unlocking significantly greater energy savings and operational performance.
According to research from the U.S. Department of Energy, buildings have the potential to achieve up to 29% energy savings through improved control and operations. The challenge has been bridging the gap between insight and action.
A new paper developed with Berkeley Lab explores how that gap can now be closed.
The Industry at an Inflection Point
The smart buildings industry is at an inflection point.
For over a decade, analytics platforms have helped building owners and operators identify inefficiencies, uncover faults, and better understand how their assets perform. These tools have delivered meaningful results, typically around 9% energy savings, but they’ve also exposed a larger opportunity.
Moving Beyond Insight
The paper outlines a fundamental shift in how smart building technology is evolving from systems that identify problems to systems that can help resolve them.
By combining advanced analytics with the ability to write back to building systems, modern platforms can now:
This shift from analytics to autonomous operations represents a significant step forward in how buildings are managed at scale.
Proven in Real Buildings
These capabilities are not theoretical.
In partnership with Cushman & Wakefield, the approach has been deployed across six commercial properties in the United States, covering more than 1 million square feet. The results demonstrate:
Importantly, these outcomes were achieved without requiring complex reprogramming of underlying building automation systems.
The Role of Data Foundations
A key enabler of this shift is the quality and structure of building data.
Commercial buildings generate vast amounts of data from HVAC systems, meters, and sensors, but that data is often inconsistent, poorly labelled, or locked within proprietary systems. Without context, it is difficult to scale analytics, let alone automate outcomes.
The approach outlined in the paper leverages a semantically enriched data layer to provide structure and meaning to building data. This foundation:
Switch Automation has been an active contributor to industry initiatives such as the Brick ontology, which underpin these emerging approaches to standardized building data modelling.
Why This Matters
As building owners and operators face increasing pressure to reduce emissions, control costs, and manage complex portfolios, the ability to move from insight to action becomes critical.
The collaboration between Switch Automation and Berkeley Lab demonstrates that the next generation of smart buildings will not just monitor performance, they will continuously optimize it.
This is how the industry begins to close the gap between what buildings currently achieve and what they are capable of delivering.
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