Details
| Original language | English |
|---|---|
| Article number | 118240 |
| Journal | Applied energy |
| Volume | 307 |
| Publication status | Published - 1 Feb 2022 |
| Externally published | Yes |
Abstract
At the energy-efficient buildings design stage, architects suffer from multi-discipline requirements and insufficient information to make proper decisions during the process. Inspired by the human nervous system's estimation mechanism, we proposed a data-driven process-based framework for decision-making support. This framework achieves the performance-oriented decision aid under uncertainties based on a general component design, consisting of three parts: the probabilistic surrogate modeling, ensemble modeling, and the model interpretation method. With the characterization of uncertainties into aleatory or epistemic based on the possibility for minimization, the component's design enables the framework to achieve dynamic interaction with users and inference toward higher intelligence to “make assumptions” in potential design space. Subsequently, it maps possible consequences of output scenarios to input variants’ causes by generating informative feedback and ensures a robust prediction under certain flexibility of incomplete inputs. We utilized the framework as an assistance system to conduct the strategic feedback of energy efficiency for building designers in different early design stages: The framework is tested on the Energy Performance Certificate (EPC) data from England and Wales (19,725,379 buildings). The result achieves a comparable forecasting performance as the SOTA machine learning and provides coherent input variants' interpretation. More importantly, during the design process, the framework enables to interactively provide building designers with expected building energy efficiency range in on-going possible design space with intervention consequences and input causes interpretation. Eventually, it drives users to operate toward higher energy-efficient building designs.
Keywords
- Energy-efficient building design, Ensemble modeling, Machine assistance, Probabilistic regression, Reasoning, Uncertainty
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Engineering(all)
- Mechanical Engineering
- Energy(all)
- General Energy
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Sustainable Development Goals
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In: Applied energy, Vol. 307, 118240, 01.02.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Machine assistance in energy-efficient building design
T2 - A predictive framework toward dynamic interaction with human decision-making under uncertainty
AU - Chen, Xia
AU - Geyer, Philipp
N1 - Funding information: We gratefully acknowledge the support of the German Research Foundation ( DFG ) for funding the researcher unit FOR 2363, grant No. GE 1652/3-2 .
PY - 2022/2/1
Y1 - 2022/2/1
N2 - At the energy-efficient buildings design stage, architects suffer from multi-discipline requirements and insufficient information to make proper decisions during the process. Inspired by the human nervous system's estimation mechanism, we proposed a data-driven process-based framework for decision-making support. This framework achieves the performance-oriented decision aid under uncertainties based on a general component design, consisting of three parts: the probabilistic surrogate modeling, ensemble modeling, and the model interpretation method. With the characterization of uncertainties into aleatory or epistemic based on the possibility for minimization, the component's design enables the framework to achieve dynamic interaction with users and inference toward higher intelligence to “make assumptions” in potential design space. Subsequently, it maps possible consequences of output scenarios to input variants’ causes by generating informative feedback and ensures a robust prediction under certain flexibility of incomplete inputs. We utilized the framework as an assistance system to conduct the strategic feedback of energy efficiency for building designers in different early design stages: The framework is tested on the Energy Performance Certificate (EPC) data from England and Wales (19,725,379 buildings). The result achieves a comparable forecasting performance as the SOTA machine learning and provides coherent input variants' interpretation. More importantly, during the design process, the framework enables to interactively provide building designers with expected building energy efficiency range in on-going possible design space with intervention consequences and input causes interpretation. Eventually, it drives users to operate toward higher energy-efficient building designs.
AB - At the energy-efficient buildings design stage, architects suffer from multi-discipline requirements and insufficient information to make proper decisions during the process. Inspired by the human nervous system's estimation mechanism, we proposed a data-driven process-based framework for decision-making support. This framework achieves the performance-oriented decision aid under uncertainties based on a general component design, consisting of three parts: the probabilistic surrogate modeling, ensemble modeling, and the model interpretation method. With the characterization of uncertainties into aleatory or epistemic based on the possibility for minimization, the component's design enables the framework to achieve dynamic interaction with users and inference toward higher intelligence to “make assumptions” in potential design space. Subsequently, it maps possible consequences of output scenarios to input variants’ causes by generating informative feedback and ensures a robust prediction under certain flexibility of incomplete inputs. We utilized the framework as an assistance system to conduct the strategic feedback of energy efficiency for building designers in different early design stages: The framework is tested on the Energy Performance Certificate (EPC) data from England and Wales (19,725,379 buildings). The result achieves a comparable forecasting performance as the SOTA machine learning and provides coherent input variants' interpretation. More importantly, during the design process, the framework enables to interactively provide building designers with expected building energy efficiency range in on-going possible design space with intervention consequences and input causes interpretation. Eventually, it drives users to operate toward higher energy-efficient building designs.
KW - Energy-efficient building design
KW - Ensemble modeling
KW - Machine assistance
KW - Probabilistic regression
KW - Reasoning
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85121419993&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2021.118240
DO - 10.1016/j.apenergy.2021.118240
M3 - Article
AN - SCOPUS:85121419993
VL - 307
JO - Applied energy
JF - Applied energy
SN - 0306-2619
M1 - 118240
ER -