Table of Content

Application of Machine Learning and Artificial Intelligence and Digital Technology for Smart Buildings

Submission Deadline: 20 November 2022 (closed)

Guest Editors

Prof. Mohammad Hossein Ahmadi, Shahrood University of Technology, Iran.
Prof. Tingzhen Ming, Wuhan University of Technology, China.
Prof. Süheyla Yerel Kandemir, Bilecik Şeyh Edebali University, Turkey.


Global warming have pushed and put pressure on researchers and decision-makers to find a practical smart energy solutions to tackle with climate change. Buildings are known to be world-wide player in terms of global warming due to the large extent of energy consumption as a result of indoor space ventilation, air conditioning (heating and cooling) and lighting. In building industry the challenge is to find a way to meet the required energy for lives at higher standards level meanwhile reducing the amount of required energy. This item is addressed in a new topic that employ different technologies and devices to integrate their systems so as to obtain the highest possible performance which is called "smart buildings". A smart building consists of several sensors which are employed to measure and monitor energy-record data and then adjustment techniques would be conducted to achieve desired conditions related to life quality in the building. The quality of life depends on thermal/visual comfort and indoor air quality. In a smart building optimization tools would be applied to reach the highest living conditions at the lowest cost in addition to the lowest energy consumption. 

Climate change and unpredicted operational problem are items that restrict the efficiency, residents' comfort, and flexibility. For this reason, artificial intelligence (AI) and machine learning (ML) are introduced to buildings to learn the patterns and find optimal values The machine learning techniques have been applied to specific stages of life-cycles in buildings. The research studies are focused on a specific aspect and do not have a thorough image for the insertion of smart technologies at the broader level. Therefore, it can be seen that the application of AI and ML methods have been missed for improving learning process in complicated and changing operational systems of a building.

Taking a big look demonstrates that the effective role of AI and digital technologies has not been conceived yet in the building sector. However, there are serious issues in digitalization that threat security and privacy. It must be noted that digitalization techniques must be designed in order to satisfy human needs and not to interrupt their lives. Accordingly, this special collection is designed to provide a showroom for a broad range of researchers to illustrate the serious challenges and new achievements in the fields of applying AI, ML, and other digitalization techniques in buildings in terms of original research articles and also critical or systematic reviews.

The main topics include but are not limited to:

• Digital energy services: new methods, models, and frameworks to understand the potentials of digital technologies and AI in the future energy conservation of buildings

• Artificial intelligence methods in building operation and maintenance.

• IoT equipped architecture, planning and engineering.

• Special building opportunities to reduce energy consumption, e.g., in data centers

• AI in infrastructure and civil constructions.

• novel techniques and methods for removing challenges in digitalization of energy systems at building and urban scales

• Big data analysis for building and facility management.

• Modeling and simulation of smart city systems

• data-based governance models that make building users agents rather than subjects of change, considering both data ownership and access models and algorithmic solutions, such as decentralized computing


• Smart Building
• Data visualization
• Intelligent infrastructure
• Internet of Things (IoT)
• Modeling and simulation of smart city systems
• Artificial intelligence
• Computational intelligence
• Machine learning and pattern recognition
• Smart and sustainable cities
• AI technologies for the smart environment

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