An overview of machine learning applications for smart buildings

Kari Alanne*, Seppo Sierla

*Corresponding author for this work

Research output: Contribution to journalReview ArticleScientificpeer-review

6 Citations (Scopus)
167 Downloads (Pure)


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 specific, 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.

Original languageEnglish
Article number103445
Number of pages12
JournalSustainable Cities and Society
Early online date12 Oct 2021
Publication statusPublished - Jan 2022
MoE publication typeA2 Review article in a scientific journal


  • Energy efficiency
  • HVAC
  • Intelligent building
  • Learning
  • Reinforcement learning
  • Smart building


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