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
Apples or Oranges? New benchmarking paper published in Applied Energy
We collected a building energy dataset (2 years, hourly data) of unprecedented size (3829 buildings) and variety (75 programs). And using machine learning, we discovered three fundamental load shape profiles that characterize the temporal energy use in any of the buildings. The existence of Read more
2019 ASHRAE Winter Conference
IEL is presenting two seminar talks at the 2019 ASHRAE Winter conference in Atlanta. PhD student Jose, is presenting: Sunday, January 13, 2019 Seminar 15: Presentation 2: LightLearn: Reinforcement Learning for Occupant Centered Control and Controller Performance Metrics 11:20AM–11:40AM, GWCC, Read more
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