From the poster: Room-level thermal modeling, HVAC model elements, water, CO2 balances, etc. ==> ~10,000 lines of Python code.
“Physically based modeling for predictive simulation of a net-zero home,” by Professor Raymond Adomaitis and his graduate student Alan Uy, won the Best Poster Award in the Computing and Systems Technology Division at the 2017 AIChE Annual Meeting held in Minneapolis in October 2017. The research represented on the poster was undertaken in the context of the 2017 Solar Decathlon, in which the University of Maryland placed second overall and first among entries from the United States.
Maryland’s entry in the contest was the “reACT: resilient Adaptive Climate Technology” house. One objective of reACT’s design was the ability to adapt to both the location in which it was constructed as well as an environment that will change continuously over multiple timescales.
To maximize measures of sustainability, reACT had to be able to use or recycle low-value resources that would otherwise go to waste. Adaptability means self-awareness, and so reACT also had to be able to predict its state over a reasonable time horizon so that it could work with the homeowners on the best use of resources throughout the day, or to carry out those tasks autonomously.
Adomaitis and Uy developed a predictive physically based model of this Solar Decathlon house and demonstrated its utility in scheduling house resources.
| Download a PDF of the poster |
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