• GitHub
  • Home
  • People
  • Research
  • Publications
  • Courses
  • News
  • Contact
  • Internal
UT Shield

Intelligent Environments Laboratory

The University of Texas at Austin
  • Home
  • People
    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
  • Research
  • Publications
  • GitHub
  • Courses
  • News
  • Contact

October 24, 2018, Filed Under: Publication

LightLearn published in Building & Environment

We have investigated experimentally the use of Reinforcement Learning for lighting control in our own offices. The results are awesome and now published in Building & Environment.

J. Park, T. Dougherty, H. Fritz, Z. Nagy, LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning,
Building and Environment,2018,

DOI: https://doi.org/10.1016/j.buildenv.2018.10.028

Abstract

In commercial buildings, lighting contributes to about 20% of the total energy consumption. Lighting controllers that integrate occupancy and luminosity sensors to improve energy efficiency have been proposed. However, they are often ineffective because they focus solely on energy consumption rather than providing comfort to the occupants. An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. In this article, we introduce LightLearn, an occupant centered controller (OCC) for lighting based on Reinforcement Learning (RL). We describe the theory and hardware implementation of LightLearn. Our experiment during eight weeks in five offices shows that LightLearn learns the individual occupant behaviors and indoor environmental conditions, and adapts its control parameters accordingly by determining personalized set-points. Participants reported that the overall lighting was slightly improved compared to prior lighting conditions. We compare LightLearn to schedule-based and occupancy-based control scenarios, and evaluate their performance with respect to total energy use, light-utilization-ratio, unmet comfort hours, as well as light-comfort-ratio, which we introduce in this paper. We show that only LightLearn balances successfully occupant comfort and energy consumption. The adaptive nature of LightLearn suggests that reinforcement learning based occupant centered control is a viable approach to mitigate the discrepancy between occupant comfort and the goals of building control.

Research Highlight

Reinforcement Learning in the Built Environment

We develop reinforcement learning techniques for energy efficient operation of buildings and systems without the need for mathematical models. Despite 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 !

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
Tweets by Z0ltanNagy

Research

  • All Projects

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
ITS

301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

UT Home | Emergency Information | Site Policies | Web Accessibility | Web Privacy | Adobe Reader

© The University of Texas at Austin 2021

  • All Projects