An overview of machine learning applications for smart buildings
Bibliographic Data
| ID | 21228403 |
|---|---|
| Authors | Kari Alanne (0000-0002-5461-6201, Aalto University, corresponding author), Seppo Sierla (0000-0002-0402-315X, Aalto University) |
| Year | 2022 |
| Volume | 76 |
| Pages | 103445 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2021.103445 |
| OpenAlex | W3205185918 |
| Language | EN |
| Citations received | 8 |
| References cited | 126 |
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
Adaptability · Air conditioning · Architectural engineering · Building automation · HVAC · Systems engineering · Air Quality Monitoring and Forecasting · Building Energy and Comfort Optimization · Computer Science · Engineering · Smart Grid Energy Management · Artificial Intelligence
Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things
Umwelt as the foundation of an ethics of smart environments
Artificial Intelligence and Energy Resilience
Machine learning based on reinforcement learning for smart grids
Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning
Assessing how wind environments impact urban building energy
Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies
Twin transition in the built environment – Policy mechanisms, technologies and market views from a cold climate perspective
A novel performance indicator for the assessment of the learning ability of smart buildings
Smart technologies and urban life
Dynamic load management for a residential customer; Reinforcement Learning approach
Demand side flexibility coordination in office buildings
Demand side management through load shifting in IoT based Hems
Improved residential energy management system using priority double deep Q-learning
Can HVAC really learn from users? A simulation-based study on the effectiveness of voting for comfort and energy use optimization
Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities
Demand side management for a residential customer in multi-energy systems
Smart Cities
Organizational Learning
| Unique citing works | 8 |
|---|---|
| Citations per year | 2 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |