Machine learning integration for adaptive building envelopes

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.