Machine learning integration for adaptive building envelopes

Shane Ida Smith, Chris Lasch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the development of an Intelligent Adaptive Control (IAC) framework that uses machine learning to integrate responsive passive conditioning at the envelope into a building's comprehensive conventional environmental control system. Inital results show that by leveraging adaptive computational control to orchestrate the building's mechanical and passive systems together, there exists a demonstrably greater potental to maximize energy eficiency than can be gained by focusing on either system individually, while the addition of more passive conditioning strategies significantly increases human comfort, health and wellness building-wide. Implicitly, this project suggests that, given the development and ever increasing adoption of building automation systems, a significant new site for computational design in architecture is expanding within the post-occupancy operation of a building, in contrast to architects' traditional focus on the building's inital design. Through the development of an experimental framework that includes physical material testing linked to computational simulation, this project begins to describe a set of tools and procedures by which architects might beter conceptualize, visualize, and experiment with the design of adaptive building envelopes. This process allows designers to ultmately engage in the opportunites presented by active systems that govern the daily interactions between a building, its inhabitants, and their environment long after construction is completed. Adaptive material assemblies at the envelope are given special atention since it is here that a building's performance and urban expression are most closely intertwined.

Original languageEnglish (US)
Title of host publicationACADIA 2016
Subtitle of host publicationPosthuman Frontiers: Data, Designers, and Cognitive Machines - Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture
PublisherACADIA
Pages98-105
Number of pages8
ISBN (Electronic)9780692770955
StatePublished - Jan 1 2016
Event36th Annual Conference of the Association for Computer Aided Design in Architecture - Posthuman Frontiers: Data, Designers, and Cognitive Machines, ACADIA 2016 - Ann Arbor, United States
Duration: Oct 27 2016Oct 29 2016

Other

Other36th Annual Conference of the Association for Computer Aided Design in Architecture - Posthuman Frontiers: Data, Designers, and Cognitive Machines, ACADIA 2016
CountryUnited States
CityAnn Arbor
Period10/27/1610/29/16

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Hardware and Architecture

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  • Cite this

    Smith, S. I., & Lasch, C. (2016). Machine learning integration for adaptive building envelopes. In ACADIA 2016: Posthuman Frontiers: Data, Designers, and Cognitive Machines - Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (pp. 98-105). ACADIA.