Research Topic

Next Generation Data-Driven Digital Twins: Applications in Buildings and Energy Efficiency

About this Research Topic

Digital twins have emerged as promising artifacts in the Industry 4.0 since they can model the processes, actors, resources, and outcomes involved in complex scenarios. More particularly, in the construction and energy management disciplines, digital twins of buildings can combine the representation power of semantically rich building information models (BIM) with real time streaming data coming from building sensors. Furthermore, digital twins of buildings can be extended with learning and self-calibration capabilities in order to support the generation of dynamic and evolving surrogate models. Digital twins can, thus, advance the understanding of complex interactions between systems as well as support the implementation of simulation-intensive control procedures, such as reinforcement learning.

The goal of this Research Topic is to present the latest advances - both from academia and industry - in the area of data-driven digital twins of buildings. We aim at bringing together researchers interested in the development and application of artificial intelligence methods for the creation, exploitation, and maintenance of digital twins of buildings. More specifically, we are interested in exploring the current landscape and future proposals on machine learning and big data algorithms that automatically emulate the energy behavior of a building. This Research Topic is open to contributions generated by specialists in related areas - physics-informed learning, model predictive control, multivariate time series prediction, etc. - in order to promote interdisciplinary collaborations and cross-fertilization of ideas.

In addition to methodological and more theoretical contributions, we welcome practical applications at the intersection of artificial intelligence, construction, and energy research. Topics of interest include, but are not limited to the following:

• Digital twins and big data: methods, platforms, use cases
• Data-driven learning of digital twins
• Challenges, tools, and best practices for interacting with digital twins
• Lifecycle of digital twins: creation, maintenance, and decommission
• Evaluation of digital twins
• Integration of real-time data streams and digital twins
• Semantic digital twins
• Data assimilation and continuous calibration in digital twins
• Imprecision and uncertainty in digital twins
• Visualization of digital twins
• Practical applications of digital twins in construction
• Practical applications of digital twins in energy


Keywords: digital twins, buildings, energy, artificial intelligence, big data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Digital twins have emerged as promising artifacts in the Industry 4.0 since they can model the processes, actors, resources, and outcomes involved in complex scenarios. More particularly, in the construction and energy management disciplines, digital twins of buildings can combine the representation power of semantically rich building information models (BIM) with real time streaming data coming from building sensors. Furthermore, digital twins of buildings can be extended with learning and self-calibration capabilities in order to support the generation of dynamic and evolving surrogate models. Digital twins can, thus, advance the understanding of complex interactions between systems as well as support the implementation of simulation-intensive control procedures, such as reinforcement learning.

The goal of this Research Topic is to present the latest advances - both from academia and industry - in the area of data-driven digital twins of buildings. We aim at bringing together researchers interested in the development and application of artificial intelligence methods for the creation, exploitation, and maintenance of digital twins of buildings. More specifically, we are interested in exploring the current landscape and future proposals on machine learning and big data algorithms that automatically emulate the energy behavior of a building. This Research Topic is open to contributions generated by specialists in related areas - physics-informed learning, model predictive control, multivariate time series prediction, etc. - in order to promote interdisciplinary collaborations and cross-fertilization of ideas.

In addition to methodological and more theoretical contributions, we welcome practical applications at the intersection of artificial intelligence, construction, and energy research. Topics of interest include, but are not limited to the following:

• Digital twins and big data: methods, platforms, use cases
• Data-driven learning of digital twins
• Challenges, tools, and best practices for interacting with digital twins
• Lifecycle of digital twins: creation, maintenance, and decommission
• Evaluation of digital twins
• Integration of real-time data streams and digital twins
• Semantic digital twins
• Data assimilation and continuous calibration in digital twins
• Imprecision and uncertainty in digital twins
• Visualization of digital twins
• Practical applications of digital twins in construction
• Practical applications of digital twins in energy


Keywords: digital twins, buildings, energy, artificial intelligence, big data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

25 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

25 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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