Wendy Lin

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-111

May 11, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-111.pdf

The building industry is responsible for over 40% of the world's energy consumption, making it imperative to develop more efficient building designs to reduce energy usage and minimize environmental impact. However, the current building design process is highly fragmented, resulting in inefficient and sub-optimal designs. Although the digitalization of the building industry happened nearly at the same time as the electronics industry, the latter has become fully automated, whereas the building industry still lags behind.

This dissertation identifies the challenges in the building design process that hinder automation and proposes a platform-based approach as part of the solution. Platform-based design enables the development of common interfaces for greater consistency and efficiency in the design process, along with modularity that breaks down complex systems into smaller, more manageable modules. This facilitates easier design iteration and adaptation to changing requirements, while also allowing for the reuse of design components across different projects, saving time and resources.

In addition, the dissertation explores the potential of levels of abstraction in building energy models to better support the design process without requiring a full building model at early design stages. This can help reduce design iterations and save time and costs. Finally, we streamline the process of constructing building models as a step towards automated design. Although fully automated design is yet to be realized, this streamlined process serves as a potential starting point for achieving automation in the future.

Advisors: Costas J. Spanos

---

BibTeX citation:

@phdthesis{Lin:EECS-2023-111,
    Author= {Lin, Wendy},
    Title= {Toward Platform-based Building Design},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-111.html},
    Number= {UCB/EECS-2023-111},
    Abstract= {The building industry is responsible for over 40% of the world's energy consumption, making it imperative to develop more efficient building designs to reduce energy usage and minimize environmental impact. However, the current building design process is highly fragmented, resulting in inefficient and sub-optimal designs. Although the digitalization of the building industry happened nearly at the same time as the electronics industry, the latter has become fully automated, whereas the building industry still lags behind.

This dissertation identifies the challenges in the building design process that hinder automation and proposes a platform-based approach as part of the solution. Platform-based design enables the development of common interfaces for greater consistency and efficiency in the design process, along with modularity that breaks down complex systems into smaller, more manageable modules. This facilitates easier design iteration and adaptation to changing requirements, while also allowing for the reuse of design components across different projects, saving time and resources.

In addition, the dissertation explores the potential of levels of abstraction in building energy models to better support the design process without requiring a full building model at early design stages. This can help reduce design iterations and save time and costs. Finally, we streamline the process of constructing building models as a step towards automated design. Although fully automated design is yet to be realized, this streamlined process serves as a potential starting point for achieving automation in the future.},
}

EndNote citation:

%0 Thesis
%A Lin, Wendy 
%T Toward Platform-based Building Design
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 11
%@ UCB/EECS-2023-111
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-111.html
%F Lin:EECS-2023-111