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Intelligent Environments Laboratory

The University of Texas at Austin
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    • Prof. Zoltan Nagy, PhD
    • June Young Park
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January 31, 2019, Filed Under: Research

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 directly by the UT Energy Department to provide students with a better classroom experience and cut costs on climate control.

 

Key Security Aspects:

 

  • Users log into the app using their unique email address and password.
  • No data is store locally on the user’s phone; instead, the data is stored in Google’s Firebase database, whose privacy policy is located here: https://firebase.google.com/support/privacy/
  • In the database, data is tied to a hashed user ID, from which it is not possible to gain personal information about the user without access to our database.
  • The use of any email other than a utexas.edu email address to sign up is disallowed; this enables us to ensure that only UT related students and other persons are allowed to take part in the data submission. A user is not even allowed to log in without a valid email address.

 

Types of Data Collected:

 

  • The data we collect from users includes 4 fields: gender, height, weight, and major in school. Users enter the data on the Settings tab of our app and press the Save button to send it to our online Firebase database, where the rest of our user data is stored. Our subjects are any UT students willing to download our iOS app and submit data for the climate of the classrooms they go to.
  • We also collect location (GPS) data from our users, which we use to make sure that a user is actually in the building for which they claim to be submitting data for. Users receive giftcard rewards in return for the data they submit. This data is also only stored in our Firebase database, not locally in the phone’s memory.
  • The only time data is presented from other users is when a user views a map of their data submissions. This map shows their average comfort level for a building, and beneath that, an average comfort level across all users. However, the comfort level is the only field we pull from the data submissions, and these submissions hold no personal information either way, as it was mentioned as the userId’s in each submission are hashed and mean nothing without our locked user authentication information in our database.
  • The data that users submit is simply the building and room they’re in, the room’s perceived temperature, their preferred temperature, and whether they’re wearing a short or long sleeve shirt and short or long pants. There is no text field that they submit, only lists to choose from, sliders, and multiple choice buttons. This enables the data to retain anonymity during submission so that users can’t accidentally reveal personal information.

 

Screenshots

Research Highlight

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 Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

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air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
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