Introducing causal inference in the energy-efficient building design process
| Autor: | Xia Chen, Jimmy Abualdenien, Manav Mahan Singh, André Borrmann, Philipp Geyer |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Mechanical Engineering Architectural design Building and Construction Decision support systems ddc Methodology (stat.ME) Computational Engineering Finance and Science (cs.CE) ddc:690 Energy efficiency Dewey Decimal Classification::600 | Technik::690 | Hausbau Bauhandwerk Structural design Data integration LOCenter Electrical and Electronic Engineering Computer Science - Computational Engineering Finance and Science Statistics - Methodology Civil and Structural Engineering |
| Zdroj: | Energy and buildings : an international journal of research applied to energy efficiency in the built environment 277 (2022) Energy and buildings : an international journal of research applied to energy efficiency in the built environment |
| Popis: | "What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization. 20 pages, 10 figures |
| Databáze: | OpenAIRE |
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