Short answer

When designing buildings, especially those with critical user comfort requirements like hospitals, consider using metamodels to efficiently explore the design space and identify optimal trade-offs between user well-being and resource consumption.

Field: User-Centred Design
Source: White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) (2017)
Method: Metamodel-based optimisation using Moving Least Squares Regression (MLSR) and Genetic Algorithms (GA).
Evidence: Strong effect

A metamodel-based optimisation methodology can efficiently explore design trade-offs for thermal comfort and energy consumption in hospital buildings. This user-centred design research insight is drawn from a 2017 study published in White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York). Using Metamodel-based optimisation using moving least squares regression (mlsr) and genetic algorithms (ga)., researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing buildings, especially those with critical user comfort requirements like hospitals, consider using metamodels to efficiently explore the design space and identify optimal trade-offs between user well-being and resource consumption.

Study

User-Centred DesignHigh ImpactStrong effect

Optimising Hospital Building Performance: A Metamodel Approach to Balancing Thermal Comfort and Energy Use

A metamodel-based optimisation methodology can efficiently explore design trade-offs for thermal comfort and energy consumption in hospital buildings.

White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2017

Key Findings

01The metamodel-based approach, particularly the OSMO strategy, offers substantial time savings compared to direct search optimisation.
02Variations in spatial location, time period, and thermal comfort criteria can lead to different optimum design conditions.
03Seasonal variations significantly influence optimum building performance.

Application

Design takeaway

How to apply

Use simulation software to generate an initial set of building performance data. Train a metamodel (e.g., using MLSR) on this data. Then, use an optimisation algorithm (e.g., a genetic algorithm) with the metamodel to explore numerous design scenarios for thermal comfort and energy use, adapting criteria as needed.

Project actions

01When simulating building performance, ensure your initial sample data covers a wide and relevant range of design variables.
02Consider how you will define and measure 'thermal comfort' and 'energy use' for your specific design project.

Method & Evidence

AimTo develop and test a metamodel-based methodology for optimising building thermal and energy performance, specifically focusing on balancing thermal discomfort and energy use in hospital environments.

MethodMetamodel-based optimisation using Moving Least Squares Regression (MLSR) and Genetic Algorithms (GA).

ProcedureInitial building simulations were used to train MLSR metamodels. A genetic algorithm then optimised for minimal time-averaged thermal discomfort and energy use, presenting the optimum trade-off as a Pareto front. Adaptive coupling of dynamic thermal models (DTM) with computational fluid dynamics (CFD) was used for local thermal comfort evaluation. The 'one sample many optimisations' (OSMO) approach allowed for multiple optimisations from a single set of sample simulations.

ContextHospital building design and optimisation.

Variables

IV["Design variables (e.g., insulation levels, window size, HVAC settings)","Location, time period, thermal comfort criteria"]

DV["Time-averaged thermal discomfort","Energy use"]

CV["Building simulation software (ESP-r)","Metamodelling technique (MLSR)","Optimisation algorithm (GA)"]

Strengths & Limitations

Strengths

+Introduces a novel 'one sample many optimisations' (OSMO) approach for increased flexibility and efficiency.
+Integrates CFD with DTM for detailed local thermal comfort analysis.

Limitations

The accuracy of the metamodel depends heavily on the initial simulation data. The computational resources required for initial simulations and metamodel training can be significant.

Reliability & validity

Reliability would be assessed by repeating the optimisation process multiple times to check for consistent results. Validity would be assessed by comparing the metamodel's predictions against direct, high-fidelity simulations of the optimised designs.

Think critically

How might the 'one sample many optimisations' approach be adapted for optimising the aesthetic qualities of a product, rather than its functional performance?

Design Principles

"Employ metamodelling techniques to accelerate the exploration of complex design spaces and identify optimal solutions for multi-objective problems, particularly in user-centric applications."

This research offers a flexible and time-saving approach to complex building design challenges, particularly in sensitive environments like hospitals. By decoupling simulation from optimisation, designers can rapidly assess multiple scenarios and user comfort criteria, leading to more informed decisions that enhance occupant well-being and operational efficiency.

What This Means for Your Design

This research shows how computer models can help designers find the best ways to make buildings comfortable for people and save energy at the same time, especially in places like hospitals, by testing many options quickly.

How to use in your project

1.Reference this research when discussing the optimisation of design solutions, particularly for balancing multiple, potentially conflicting, design objectives like user comfort and energy efficiency.

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Quick Cite

Paragraph starter

The development of metamodel-based optimisation techniques, as demonstrated in the study of hospital building performance, offers a powerful approach to efficiently exploring complex design spaces. By training predictive models on initial simulation data, designers can rapidly assess numerous design variations and identify optimal trade-offs between conflicting objectives, such as enhancing user thermal comfort while minimising energy consumption. This methodology allows for a flexible 'one sample many optimisations' strategy, significantly reducing the computational effort required compared to traditional direct search methods.

Source

White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)

Numerical Optimisation of Building Thermal and Energy Performance in Hospitals

journal · 2017

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Questions About This Research

What does the research say about optimising hospital building performance: a metamodel approach to balancing thermal comfort and energy use?: When designing buildings, especially those with critical user comfort requirements like hospitals, consider using metamodels to efficiently explore the design space and identify optimal trade-offs between user well-being and resource consumption. Evidence: White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) (2017).
Why does "Optimising Hospital Building Performance: A Metamodel Approach to Balancing Thermal Comfort and Energy Use" matter for design?: This research offers a flexible and time-saving approach to complex building design challenges, particularly in sensitive environments like hospitals. By decoupling simulation from optimisation, designers can rapidly assess multiple scenarios and user comfort criteria, leading to more informed decisions that enhance occupant well-being and operational efficiency.
How can designers apply this research?: When designing buildings, especially those with critical user comfort requirements like hospitals, consider using metamodels to efficiently explore the design space and identify optimal trade-offs between user well-being and resource consumption.
What were the main findings?: The metamodel-based approach, particularly the OSMO strategy, offers substantial time savings compared to direct search optimisation.. Variations in spatial location, time period, and thermal comfort criteria can lead to different optimum design conditions.. Seasonal variations significantly influence optimum building performance.
What research method was used?: Metamodel-based optimisation using Moving Least Squares Regression (MLSR) and Genetic Algorithms (GA)..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2017 journal from White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York).
What should I do differently in my next project?: Use simulation software to generate an initial set of building performance data. Train a metamodel (e.g., using MLSR) on this data. Then, use an optimisation algorithm (e.g., a genetic algorithm) with the metamodel to explore numerous design scenarios for thermal comfort and energy use, adapting criteria as needed.
What are the limitations?: The effectiveness of the metamodels is dependent on the quality and representativeness of the initial sample simulations. The selection of design variables and their ranges can influence the optimisation outcomes.