AI-Driven Distributed Optimization for Building Energy Management

Buildings account for approximately 40% of global energy consumption and contribute significantly to greenhouse gas emissions, making their efficient operation critical to achieving climate neutrality goals. This challenge extends to cultural heritage buildings—historic structures, museums, monuments, and conservation sites—which require specialized AI-driven preservation strategies that balance energy efficiency with the protection of irreplaceable architectural features and artifact integrity. The integration of artificial intelligence with distributed optimization techniques represents a transformative approach to building energy management, enabling real-time coordination of heating, ventilation, air conditioning (HVAC) systems, lighting, and renewable energy sources across multi-building campuses, urban districts, and heritage sites. This paradigm shift is particularly timely as buildings become increasingly interconnected through Internet of Things (IoT) devices and smart grid infrastructure, generating vast amounts of operational data that can be leveraged for intelligent decision-making. Multi-agent systems, predictive control, and edge computing offer unprecedented opportunities to create adaptive and resilient energy management solutions that can significantly reduce operational costs while supporting grid stability and decarbonization efforts.

This Special Issue, titled AI-Driven Distributed Optimization for Building Energy Management, will provide an overview of existing knowledge on new approaches for building energy management, with particular attention given to the unique requirements of cultural heritage preservation. Original research, theoretical and experimental work, case studies, and comprehensive review papers are invited for possible publication. Relevant topics to this Special Issue include, but are not limited to, the following subjects:

• Developing scalable AI algorithms that can handle the computational complexity of large-scale building networks, including heritage sites, while preserving occupant privacy and respecting conservation constraints.
• Managing the uncertainty inherent in renewable energy generation, occupancy patterns, and dynamic electricity pricing, particularly in heritage buildings with sensitive environmental requirements.
• Ensuring robust performance under communication failures or cyber–physical attacks in both modern and historically significant structures.
• Bridging the gap between theoretical optimization frameworks and practical implementation in existing building stock, including retrofit solutions for protected heritage buildings where invasive modifications are restricted.
• Balancing multiple competing objectives such as energy efficiency, thermal comfort, indoor air quality, peak demand reduction, and for cultural heritage site preservation of structural integrity, artifact conservation, and compliance with heritage protection regulations
• Research on energy efficiency and sustainability in the artificial intelligence-assisted safeguarding of cultural heritage assets.
• AI-driven approaches that support the safeguarding of sustainable cultural heritage.

Dr. Otilia Elena Dragomir
Prof. Dr. Renato De Leone
Dr. Yelda Durgun Şahin
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.

• distributed optimization
• artificial intelligence
• building energy management systems (BEMS)
• model predictive control
• multi-agent systems
• demand response
• HVAC optimization
• renewable energy integration
• edge computing
• IoT sensors
• energy efficiency
• thermal comfort
• occupancy prediction
• deep reinforcement learning
• cyber–physical systems
• microgrid coordination
• load forecasting
• building-to-grid integration