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id acadia16_98
authors Smith, Shane Ida; Lasch, Chris
year 2016
title Machine Learning Integration for Adaptive Building Envelopes: An Experimental Framework for Intelligent Adaptive Control
source ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 98-105
summary 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. Initial results show that by leveraging adaptive computational control to orchestrate the building’s mechanical and passive systems together, there exists a demonstrably greater potential to maximize energy efficiency than can be gained by focusing on either system individually, while the addition of more passive conditioning strategies significantly increase 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 initial 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 better conceptualize, visualize, and experiment with the design of adaptive building envelopes. This process allows designers to ultimately engage in the opportunities 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 attention since it is here that a building’s performance and urban expression are most closely intertwined.
keywords model predictive control, reinforcement learning, energy performance, adaptive envelope, sensate systems
series ACADIA
type paper
email shaneida@u.arizona.edu
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last changed 2016/10/24 11:12
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