It’s no secret that buildings account for a significant portion of global carbon emissions every year – the built environment currently contributes about 40 percent of all CO2 released into the atmosphere every year. And with 111 million existing buildings in the U.S. today (only a handful of which have may have the infrastructure or technology necessary to achieve effective decarbonization), the need for retrofitting at scale becomes paramount. That translates into roughly 10,000 buildings a day – all of which need to become optimized in order to meet these 2050 goals. Manually tuning and adjusting one room at a time within these buildings just won’t cut it.
The key to all of it, we believe, is to make buildings “self-healing” in a sense through the implementation of Automated System Optimization (ASO) – technology and infrastructure that allows the building to collect data on itself, recognize inefficiencies, and make adjustments. To do that at scale, there are 3 critical aspects that need to be in place within the buildings:
Interoperable semantic models
Bidirectional independent data layers
We’ll touch on each of them below:
One of the most exciting areas for those of us involved in the cause of reducing building emissions has been the emergence of self-correcting applications, which leverage advanced technologies to optimize building performance continuously. These applications monitor and analyze data from various sensors and systems within a building, allowing for real-time adjustments and predictive maintenance. By proactively identifying and rectifying inefficiencies, energy waste is minimized, and carbon emissions are reduced.
Dr Jessica Granderson and her team at Lawrence Berkeley Laboratory are among the pioneers of the field. Their work on Self-Correcting Controls Technology in partnership with the Department of Energy has helped to boost the realistic prospects that buildings can decarbonize to the degree that optimized buildings can make a significant contribution to U.S. efforts to meet overall 2050 net zero goals. In her view, self-correcting controls are the gamechanger that will allow for the effective retrofitting of 111 million buildings in the U.S. She says:
“A future with self-optimizing buildings is at-hand. Of course, buildings vary wildly, but there are proven strategies to deliver automated fault detection, automated fault correction, and optimal control, for a great majority of the buildings large enough to have a Building Automation System. We don’t need to wait for the technologies to mature – they are ready to deploy.”
–Dr. Jessica Granderson, Lawrence Berkeley National Laboratory
How do automated optimal control applications work? Leveraging the controls technologies that are already in place in most larger buildings, advanced applications can adjust heating, ventilation, and air conditioning (HVAC) systems to dynamically adapt temperature and airflow based on inputs including occupancy patterns, weather forecasts, and indoor air quality. These optimizations can be “tuned” to ensure optimal comfort while minimizing energy consumption.
Interoperable Semantic Models
Self-correcting controls are certainly one critical aspect of reducing building emissions at scale. But such applications won’t work unless they have accurate “maps” of the building elements – or what’s known as interoperable semantic models. In other words, for self-correcting applications to be effective, they have to be given a more granular model (interoperable semantic model) of the systems – i.e., how the building and its spaces/rooms are being used specifically. Many times, for example, buildings are constructed for one purpose, then changed. As a result, any model of the original building purpose would be out of synch with the current usage. That has to be kept up to date.
Interoperable semantic models enable seamless and accurate communication and data exchange between different building systems and devices. By utilizing standardized protocols and data formats, disparate systems can “speak the same language,” facilitating efficient coordination and integration. This interoperability unlocks the potential for comprehensive building management and optimization.
Recently, ACE IoT’s own Andrew Rodgers had the honor of being invited as a reviewer for the Controls track of the U.S. DOE (Department of Energy) BTO (Building Technologies Office) annual peer review, where many of the projects presented at the review were contributing to the development of a semantic interoperability framework. Says Andrew:
“At the DoE Peer Review, where important research projects are presented one-after-the-other, it was abundantly clear that researchers at National Labs across the country are homing in on the central import of an interoperable semantic model for buildings. A reference framework for a semantic interoperable model seems close at hand. At the Peer Review, I told everyone who would listen that DoE should fund a cross-lab call designed to finalize the reference framework. It is difficult for me to imagine a more important contribution to the effort to optimize 100 million buildings by 2050.”
–Andrew Rodgers, Co-Founder, ACE IoT Solutions
Read more about Andrew’s experience at the DoE 2023 Peer Review here.
Bidirectional Independent Data Layers (IDLs)
Finally, bidirectional independent data layers (IDLs) act as the backbone of a building’s data infrastructure. IDLs are designed to collect, aggregate, and analyze data from various sensors, systems, and applications, providing valuable insights for automated decision-making and optimization.
What’s the relationship between the IDL, the self-correcting application, and the semantic model?
If you have all the elements in place, you essentially have the main pieces necessary to enable “self-healing” buildings. The IDL, infrastructure that’s established and supported by a growing number of companies including, of course, ACE IoT Solutions, both collects data from the building control systems (through, for example, a gateway from an overlay network running on top of a BAS and from the interoperable semantic model. The IDL then passes that data to the self-correcting controls application. (The IDL basically interprets the semantic model into values that allows for the self-correcting measures in building to function.)
Independent data layers are bidirectional in the sense that the self-correcting controls application then sends the instructions to the building control systems back through the IDL so the energy usage of the building can be automatically optimized. Moreover, IDLs also support data sharing between buildings, allowing for industry-wide best practices, standards, and benchmarks, fostering collective progress toward building decarbonization.
“To meet our 2050 goals we will need demand-flexible low-emissions performance, and bi-directional analytics and control are key to scaling the transformations we need to get there.
–Dr. Jessica Granderson, Lawrence Berkeley National Laboratory
The relationship between self-correcting applications, interoperable semantic models, and bidirectional independent data layers is symbiotic. By combining these three components into a cohesive approach, we believe buildings can undergo a holistic transformation toward energy efficiency and sustainability. Together, they form a powerful toolset that empowers building owners, (as well as their consultants) to contribute to the 2050 building decarbonization goals. At ACE IoT Solutions, we’re excited about the opportunity that the new technologies present and we look forward to being a pivotal player in helping to realize this vision and securing a more sustainable planet for future generations.
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