Building performance simulation in the brave new world of artificial intelligence and digital twins: A systematic review

Wilde, Pieter de

doi:10.1016/j.enbuild.2023.113171

Cited by 37 publications

(15 citation statements)

References 142 publications

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“…The emergence of new digital domains of knowledge in the built environment has brought about novel terminology and ideas, reshaping our understanding of established technologies [29]. This trend is especially noticeable in the domain of machine learning models for energy in buildings, which are frequently developed as "black boxes".…”

Section: Digital Twins In Buildings Mandv and Interpretable Data-driv...mentioning

confidence: 99%

“…Regarding the digitalisation of building assets, building information modelling (BIM) has developed over time as a multi-faceted concept [25] that presents open challenges from a cognitive [26] and knowledge management standpoint. The recent developments from BIM to digital twins (DTs) [27], involving multiple domains of knowledge within the building sector and across life cycle phases (from "cradle to cradle" [28]), are promising when seen from the perspective of building performance modelling [29], and data-driven methods are becoming [29] crucial instruments [30] for improving energy efficiency [31], fostering renewable energy source penetration and enhancing energy flexibility, by means of data-driven analytics.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Manfren,

Gonzalez-Carreon,

James

2024

Energies

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Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review’s findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods.

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Section: Digital Twins In Buildings Mandv and Interpretable Data-driv...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Manfren,

Gonzalez-Carreon,

James

2024

Energies

View full text Add to dashboard Cite

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“…To understand digital twins for building energy simulation either for single buildings or at an urban scale, it is important to know some concepts, like building information model (BIM), building energy model (BEM), urban building energy model (UBEM), and Internet of Things (IoT). The first one, the BIM, is a comprehensive digital representation of a building and typically contains information about its geometry and systems, spaces and zones, and the project structure/schedule [15]. However, BIM does not contain the entire energy information about the building.…”

Section: Building Energy Modelling and Pv Systems In The Urban Enviro...mentioning

confidence: 99%

Integration of PV Systems in the Urban Environment—A Review

Rodrigues,

Palmero-Marrero,

Oliveira

2024

Preprint

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Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The urban heat island development is introduced and related with the existence of PV systems. It is concluded that data acquisition is still a weakness in urban building energy modelling, especially in what concerns non-geometric data, which leads to a major source of errors. The availability of data is a challenge because frequently they are not accessible, due to privacy, high-cost or other reasons. Also, methodologies to develop precise archetypes for creating urban building energy models are lacking.

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“…Studies have suggested that, AI-driven building automation could lead to energy savings of 20-30% in commercial buildings [45] while in residential buildings, a savings of 8.48 % in energy and 7.52 % in cost [46]. According to De Wilde [47], in the aspect of maintenance and repairs, considered to be part of building operation stage, AI helps in prediction and diagnosing of maintenance and repair needs, thereby reducing downtime and improving building performance. Typical example is where AI-powered predictive maintenance systems can analyze data from building sensors and predict when equipment is likely to fail, allowing for proactive maintenance and reducing equipment downtime.…”

Section: Artificial Intelligence In Sustainable Buildingmentioning

confidence: 99%

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

Adewale,

Ene,

Ogunbayo

et al. 2024

Preprint

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With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.

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Building performance simulation in the brave new world of artificial intelligence and digital twins: A systematic review

Cited by 37 publications

References 142 publications

Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Integration of PV Systems in the Urban Environment—A Review

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

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