Data-Driven Intelligent Energy Management for Low-Carbon Buildings

Dear Colleagues,

Intelligent approaches are pivotal for accelerating the transformation of the building sector toward low-carbon and climate-resilient energy systems by integrating renewable energy sources, enhancing energy efficiency and flexibility, advancing building electrification and demand response management, and implementing advanced controls. The reliance on conventional energy management systems based on predefined, static rules offers minimal flexibility, particularly in the configuration of complex energy systems and in the presence of dynamic parameters such as occupancy patterns, weather conditions, non-programmable energy sources, evolving energy markets, and uncertainties and externalities related to climate change impacts. Hence, recent advances in data-driven approaches such as the application of artificial intelligence (AI), machine learning (ML), digital twinning, internet of things (IoT), and data analytics provide promising opportunities to handle such complex interactions between internal building energy systems and external adjacent systems at neighbouhood and urban scale efficiently through development of adaptive, predictive and responsive smart energy solutions for building decarbonization goals.

This Special Issue aims to present cutting-edge research on data-driven methods for smart energy management systems and their contributions to energy efficiency, energy flexibility, life cycle cost, and environmental emissions mitigation, as well as decarbonization solutions at the building and urban scales. Therefore, original research and review papers addressing energy performance forecasting, optimization, renewable integration, energy storage, advanced and predictive controls, and digital twins are particularly welcomed. Research combining data-driven methods and smart solutions with building physics, life cycle assessment and techno-economic analysis, and occupant-centric strategies is highly encouraged. The Special Issue seeks to advance interdisciplinary knowledge and practical smart intelligent solutions to accelerate the transition towards smart and low-carbon buildings.

We invite high-quality submissions in areas including, but not limited to, the following:

• Data-driven and AI-based energy management;
• Machine learning and predictive energy modeling;
• Smart building, energy retrofitting and optimization;
• Renewable energy and storage integration;
• Building-grid interaction and demand response management;
• Digital twins for building energy management systems;
• Adaptive and predictive models for energy system management;
• Intelligence for building decarbonization, life cycle assessment and techno-economic analysis.

Dr. Fabrizio Leonforte
Prof. Dr. Mohammadjavad Mahdavinejad
Dr. Harold Enrique Huerto-Cardenas
Dr. Hashem Amini Toosi
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-anonymized 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.