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.
The most common types of IoT data being gathered and stored are device information, time series data, and contextual data.
Auto manufacturers are adopting smart manufacturing strategies to modernize facilities and improve the sustainability of their operations.
XAI provides full insight into what decisions a system is making and why, which, in turn, identifies what data can be trusted and what data should be cast aside.
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.
