An overview of machine learning applications for smart buildings

Kari Alanne, Seppo Sierla
2021 Sustainable cities and society  
The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a
more » ... c, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings' adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation. 'intelligent building' (IB) and 'smart building' (SB) (Al Dakheel et al. (2020) ; Wang et al. ( 2020 ))). A shift towards the implementation of artificial intelligence (AI) trained by machine learning algorithms is recognized as one of the major trends of development (Karpook, 2017) . Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced useful for the autonomous control networks of buildings (Han et al., 2019) . Quite a few review articles have been published with various perspectives on smart buildings. A quick look at the most relevant review articles in the field reveals that most of them focus on issues such as hardware technologies, monitoring, forecasting, modelling, building energy management, and applications of machine learning (Alawadi
doi:10.1016/j.scs.2021.103445 fatcat:mcyollihdfcnrmqp3mmz4bl3m4