Colin Riker
Pronouns:
Research Mentor(s): Lars Junghans
Research Mentor School/College/Department: Architecture / AUP
Program:
Authors: Colin Riker
Session: Session 6: 3:40 pm – 4:30 pm
Poster: 86
Abstract
A major concern in the effort to reduce energy usage globally is how to efficiently heat and cool our built environment. To reduce the energy intensity and thus the environmental impact of our homes, offices, etc. Our group is looking to see how machine learning, embedded systems, and prior passive thermal management solutions can be tied together, to more effectively manage a home’s climate control system. While many of these technologies have been used in specific control system schema, the primary novel interest is to bring all of these technologies together to work in a tightly coupled-cooperative method to maximize efficiency gains. To that end, this project focused on an active microcontroller-managed heat recovery system. The methodologies for testing are mainly through data collection of the built system and proof of concept prototypes While the specific focus of this project is on one component of the whole residential scale idea, it also provides a foundation validating the hypothesis outlined in the 22-26 Project which could then be applied much more broadly. Our built environment is more or less everywhere and considerable resources are consumed daily to manage it. Reducing that demand is the goal of the research and doing so could provide widespread good in an environmental sense.
