Decarbonization continues to be one of the largest buzzwords floating around the plumbing and HVAC industries in 2024. You may have noticed our cover story this month tackles this growing trend.

There’s no question that government policy is behind the drive to decarbonize commercial buildings — especially with the DOE’s April release of the national blueprint for decarbonizing U.S. buildings by 2050. However, there has been some industry pushback on realistically meeting these sustainability goals. One consensus among industry experts is connected controls can make a big difference in lowering energy consumption.

Which leads into another hot industry topic, Artificial Intelligence, and whether or not AI can impact decarbonization goals. A study published last month seeks to answer that very question, as authors Chao Ding, Jing Ke, Mark Levine and Nan Zhou evaluate AI’s potential in the building sector.

A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation and design and construction. Six scenarios were used to estimate energy and emissions savings across multiple climate zones.

According to the study, adopting AI could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. AI has the potential to reduce costs across various stages of the construction process, mitigate risks and enhance health and welfare benefits, the study said.

“We believe that AI can improve energy efficiency and reduce carbon emissions through two main approaches: (1) AI helps scale up the best available technologies and practices. Because it can significantly help to scale up the technologies and speed adoption by reducing the construction and labor costs. Thus, it can lead to larger-scale penetration of efficient technologies; (2) AI can further improve and optimize design, construction and operation over the entire buildings’ lifecycle, which brings in additional savings,” the study said. “Moreover, the interactions between building occupants and building components are nonlinear and difficult to capture using traditional rule-based control algorithms. With advanced AI algorithms such as deep learning and reinforcement learning, the AI model can itself learn from operational data and evolve itself with continuous live data to optimize objective functions and improve performance.”

Just take a look at real-world examples, such as Hilton Hotels. Hilton Worldwide has implemented an AI-driven energy management system across its properties. The system uses machine learning algorithms to analyze data from HVAC systems, optimizing their performance and reducing energy consumption. This initiative has helped Hilton save over $1 billion in cumulative cost savings through AI-driven management of energy, water and waste since 2008. It has also resulted in a 30% reduction in carbon emissions and waste output and a 20% reduction in water consumption and energy usage.

The integration of AI in commercial building energy management is still in its early stages, but the potential is vast. As technology continues to evolve, AI systems will become more sophisticated, providing even greater insights and offer a path to significant energy savings to meet decarbonization goals.