B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards autonomic orchestration of machine learning pipelines in future networks
Machine learning (ML) techniques are being increasingly used in mobile networks for network planning, operation, management, optimisation and much more. These techniques are realised using a set of logical nodes known as…
BIG-bench Machine LearningManagementPrivacy PreservingAchilles Heels for AGI/ASI via Decision Theoretic Adversaries
As progress in AI continues to advance, it is important to know how advanced systems will make choices and in what ways they may fail. Machines can already outsmart humans in some domains, and understanding how to safely…
Intelligence Quotient and Intelligence Grade of Artificial Intelligence
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue o…
SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks
Automatic network management driven by Artificial Intelligent technologies has been heatedly discussed over decades. However, current reports mainly focus on theoretic proposals and architecture designs, works on practic…
ManagementA Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings
Energy optimization leveraging artificially intelligent algorithms has been proven effective. However, when buildings are commissioned, there is no historical data that could be used to train these algorithms. On-line Re…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning