This paper led by PhD student Farid Khosravikia from Patricia Clayton's group has been published in Seismological Research Letters. Check it out y'all. Farid developed neural network based models for earthquakes. DOI: https://doi.org/10.1785/0220180218 This paper, as well as our recent Read more
UT Energy App – Privacy Policy
The Intelligent Environments Laboratory is releasing the first version of the UT Energy App. This app is intended to provide a way to collect information on climate/temperature and consequent comfort levels for students in UT classrooms and other campus buildings. This data will be used Read more
Temperature-preference learning with neural networks for occupant-centric building indoor climate controls
Our paper led by Yuzhen Peng from ETH Zurich has been recently published in Building & Environment. We propose an intelligent building control strategy with machine learning techniques together with its design process for creating occupant-centric indoor climate. We demonstrate 4%–25% energy Read more
The Future of Building Energy Efficiency: Smart Building or Building Smart?
A panel discussion at the UT Energy Week on 2/5, moderated by Dr Nagy, and sponsored by The University of Texas at Austin Student Chapter of ASHRAE Buildings account for 40% of energy consumption in the United States. In the growing age of ‘smart’ technologies and sustainable design, how do these Read more
Fault detection and diagnostics of air handling units using machine learning and expert rule-sets
To reduce HVAC energy inefficiencies, fault detection and diagnostics (FDD) has become a growing field of interest. In particular, air handling units (AHU), devices that circulate air and regulate room temperature and humidity, are the primary focus of most HVAC FDD systems. A data-driven FDD for Read more
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