‘The building performance gap: Are modellers literate?’

Wilde, Pieter de

doi:10.1177/0143624417728431

Cited by 13 publications

(6 citation statements)

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“…Of all these advancements, digital twins (DTs) are particularly notable for their ability to enable the comparison between simulated (and/or predicted) and measured data in buildings, providing insights on the actual performance of physical assets and enabling a continuous monitoring and, at least potentially, improvement process. This ability to learn from "gaps" between predicted and actual performance is particularly interesting with respect to the debated question of "performance gap" in buildings [58][59][60] and can act as a catalyst for innovation within the building research community. However, an effective implementation of the DT concept in buildings is challenging due to the need to incorporate multiple data streams from building information modelling (BIM), building performance simulation, machine learning tools/platforms, etc.…”

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

confidence: 99%

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%

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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“…Sharing knowledge among actors is crucial when addressing building energy performance in a comprehensive way, considering both human and technical factors [31]. In fact, the impact of occupants has to be considered from multiple stand-points [32] and users' behaviour can determine both "re-bound" [33] and "pre-bound" effects [34,35], that can create a substantial difference between expected and measured performance, which can be inscribed in the general category of "performance gaps" [36][37][38]. A "performance gap" can be found in all the stages of building life cycle [39] and the use of standardized assumption in modelling, e.g., to create Energy Performance Certificates, has to be critically questioned when using them to estimate actual energy consumption and potential savings [40].…”

Section: Background and Motivationmentioning

confidence: 99%

“…It is therefore necessary to structure energy performance analysis with respect to both human and technical factors. In turn, this is important, for example, to address properly the gap between design and measured performance, i.e., the performance gap [36][37][38], introduced in Section 2. Further, the concept of statistical "Reference Buildings" [65] (RB) must be introduced to enable building performance benchmarking at multiple scales.…”

Section: Building Energy Performance Analysis At Multiple Scalesmentioning

confidence: 99%

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

2021

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Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.

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“…From a life cycle perspective, the environmental impact of buildings should account for both embodied (products and construction practices) and operational energy consumptions. Both the measurement of embodied energy and carbon equivalent of buildings (De Wolf et al, 2017) and operational energy consumption (de Wilde, 2017;Imam et al, 2017) represent critical (and debatable) issues that can ultimately determine a relevant "performance gap." Finally, energy transition strategies have to address complementarities (Markard and Hoffmann, 2016) which are crucial for the co-evolution of built environment and energy infrastructures (Junker et al, 2018;Dominković et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

2020

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The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

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‘The building performance gap: Are modellers literate?’

Cited by 13 publications

References 1 publication

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

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

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