Topic Editors
Digital Twin of Building Energy Systems
Topic Information
Dear Colleagues,
With the rapid advancement of Internet of Things and artificial intelligence technologies, building energy systems are undergoing a profound transformation from automation to intelligence. Digital twins serve as the bridge connecting physical structures with virtual spaces, offering a novel approach to bridging the performance gap between architectural design and operational reality. However, when dealing with complex dynamic systems such as HVAC, key bottlenecks remain in constraining their large-scale application. These include how to construct high-precision models that combine physical interpretability with computational efficiency, how to utilise real-time streaming data for continuous calibration and adaptive updating of models, and how to establish a scientifically sound evaluation framework for digital twin fidelity. Particularly in demanding scenarios such as data centre cooling control and building flexible load discovery, the real-time responsiveness and predictive capabilities of digital twins prove indispensable. To this end, this Topic centres on the cutting-edge theme of digital twins for building energy systems. It explores implementation methodologies across multiple dimensions—including physical-data fusion modelling, online automated calibration, multidimensional fidelity assessment, and engineering applications in key scenarios—with the aim of fully unlocking data value and enhancing the perception, prediction, and optimisation capabilities of building energy systems.
Methodological studies, experimental testing, and research reviews are all welcome to be submitted to this Topic. The research themes for this Topic include, but are not limited to, the following:
- High-precision modelling techniques for building energy systems;
- Physical Information Machine Learning (PIML) methodology;
- Continuous calibration and correction of digital twin models;
- Digital twin fidelity evaluation methods and metrics;
- Digital twin implementation and optimisation for data centre cooling systems;
- Modelling and real-time quantification of building flexibility potential;
- Fault Diagnosis (FDD) based on digital twins;
- Predictive maintenance and optimised control for building energy systems.
Prof. Dr. Zhe Tian
Dr. Jide Niu
Dr. Jianli Chen
Dr. Yakai Lu
Topic Editors
Keywords
- digital twin for building energy
- high-precision modelling
- continuous calibration technology
- physical information machine learning
- fidelity assessment
- flexible submarine quantification
- digital twin and optimisation of data centre cooling systems
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Buildings
|
3.4 | 5.6 | 2011 | 14.7 Days | CHF 2600 | Submit |
Energies
|
3.9 | 8.3 | 2008 | 16.7 Days | CHF 2600 | Submit |
Sci
|
4.1 | 5.4 | 2019 | 28.2 Days | CHF 1400 | Submit |
Smart Cities
|
6.6 | 13.0 | 2018 | 25.1 Days | CHF 2000 | Submit |
Sustainability
|
4.1 | 8.9 | 2009 | 16.9 Days | CHF 2400 | Submit |
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