Using predictive software for building energy management usually entails solving an optimization problem--with the main tradeoff being cost versus comfort. One company, BuildingIQ, is having success solving the challenge and is looking to make automated electricity demand response (autoDR) a reality.
A new research collaboration will focus on Superlearners, a foundational machine learning technology that enables autonomy for all building applications.
Energy harvesting IoT solutions play a crucial part in not only creating better workplaces and optimizing the energy consumption of buildings, but also in significantly reducing yearly operating costs.
By 2026, the number of sensors deployed in smart buildings will exceed one billion. As a result, more AI deployment will follow to provide insights into the building data they collect.
Investments in analytics and data management companies continues to be strong with many realizing significant late-stage funding rounds.
Modern smart building applications are helping building designers, owners, and managers digitally transform their operations to realize enhanced benefits.
Companies must realize that smart buildings can deliver more value in space optimization and employee productivity than in merely reducing energy costs.
Autonomous buildings aim for a higher level of automation than smart buildings, optimizing environmental impact, enhancing comfort, and increasing security.
While AI is seen as the ultimate future of smart building management, there's still a lot of work to be done before it takes hold..
Interest in using AI to manage buildings is on the rise because it can help lower costs during an economic downturn and reduce carbon emissions.
