Universal design space exploration for building energy design

Brett Bass, Leland Curtis, Joshua New, Stet Sanborn, Peter McNally

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper proposes novel improvements to existing Design Space Exploration (DSE) workflows that enable detailed performance-analysis at the speed of design for all projects at little cost. The authors refer to this methodology as Universal Design Space Exploration (UDSE). Rather than apply DSE to a project-specific challenge, UDSE enables a single pre-simulated design space to be applicable across many projects. The novel scalability of these “universal” design spaces justify investment in ML-powered apps that make pre-simulated analysis instantly accessible, affordable, and impactful across multiple projects. This research showcases the feasibility and potential benefit of UDSE by applying it to the challenge of early conceptual energy modeling. First, a group of experts crafts the input parameters and output metrics of a massive Design Space so that it encompasses the common problem. Then an automated parametric simulation workflow is developed to model and simulate any combination of input parameters. Several hundred thousand iterations are then simulated and analyzed. The result of this analysis guides the design of a prototype app which is powered by an AI surrogate model that allows users to receive instantaneous analysis about any design contained within the design space. This research shows that it is feasible to simulate the massive design spaces required by UDSE using currently available computational resources. We show that the surrogate modeling process is capable of accurately extending relatively limited simulation data to fully map the design space. We also show that these surrogate models can be effectively integrated into custom apps that can automate advanced DSE analysis and deliver insights to design teams in real-time. This paper concludes that UDSE offers a novel and scalable approach to early conceptual performance analysis.

Original languageEnglish
Article number105977
JournalJournal of Building Engineering
Volume68
DOIs
StatePublished - Jun 1 2023

Funding

This work was funded by field work proposal CEBT105 under the Department of Energy Building Technology Activity Number BT0305000. We would like to thank Amir Roth and Madeline Salzman for their support and review of this project. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725 . This work was funded by field work proposal CEBT105 under the Department of Energy Building Technology Activity Number BT0305000. We would like to thank Amir Roth and Madeline Salzman for their support and review of this project. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under Contract Number DEAC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725 . This manuscript has been authored by UT-Battelle, LLC, under Contract Number DEAC05-00OR22725 with the US Department of Energy . The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

  • Building design
  • Energy
  • Machine learning

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