B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings
This addresses the problem for engineers and architects in the smart building industry by providing a structured framework, though it is incremental as it organizes existing AI techniques rather than introducing new algorithms.
The paper tackles the lack of systematic guidance for applying AI in smart buildings by introducing B-SMART, the first reference architecture for autonomic smart buildings, which decouples functional layers into an autonomic control loop and is demonstrated through a case study to accelerate AI integration.
The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building.