Artificial Intelligence for Intelligent Systems and Energy-Efficient Buildings
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 15
Special Issue Editors
Interests: artificial intelligence; machine learning; adaptive control; predictive control; intelligent buildings; indoor environmental quality; daylight utilisation; HVAC systems; data-driven modelling; energy-efficient buildings
Interests: intelligent systems; artificial intelligence; machine learning; neural networks; computer vision systems; sensors; control systems; Industry 4.0; intelligent buildings
Interests: energy saving; renewable energies; building automation; optimization; AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing demand for energy-efficient systems and buildings, together with the rapid digital transformation of the built environment, creates an urgent need for new approaches to design, operation and optimisation. The integration of artificial intelligence (AI) into complex technical systems and modern buildings represents a rapidly evolving and highly relevant research area with significant scientific and practical importance.
Modern building systems increasingly rely on interconnected architectures that integrate sensor networks, continuous data acquisition and advanced computational methods to monitor, analyse and optimise performance. These systems enable adaptive, data-driven and real-time decision-making, allowing building operation to respond dynamically to changing environmental conditions and user requirements.
This Special Issue focuses on the application of AI in intelligent and energy-efficient systems, with particular emphasis on building-related applications. The scope includes AI-based modelling, predictive and adaptive control, indoor environmental quality, energy optimisation and smart building operation. Special attention is given to sensor networks, automated data processing and machine-learning techniques for analysing and optimising system performance.
Contributions are invited on AI-driven modelling, intelligent control systems, indoor environment management and experimental validation in real building environments. The aim is to support the development of efficient and intelligent building systems through AI-enabled approaches.
Dr. Pavol Belany
Dr. Roman Budjac
Dr. Xiaolei Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- energy-efficient buildings
- data-driven modelling
- intelligent buildings
- adaptive control
- predictive control
- machine learning
- intelligent systems
- control systems
- neural networks
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