Smart building with intelligent indoor lighting control system using reinforcement learning simulations

In most conventional indoor environments today, such as households or office buildings, the lighting control systems deployed incorporates digitalization through motion sensors or wireless communication technology to operate or control the lighting’s operation and brightness. Such utilization often...

Full description

Saved in:
Bibliographic Details
Main Author: Chan, Keno Jia Nuo
Other Authors: En-Hua Yang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/150183
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-150183
record_format dspace
spelling sg-ntu-dr.10356-1501832021-06-06T09:13:18Z Smart building with intelligent indoor lighting control system using reinforcement learning simulations Chan, Keno Jia Nuo En-Hua Yang School of Civil and Environmental Engineering EHYANG@ntu.edu.sg Engineering::Environmental engineering In most conventional indoor environments today, such as households or office buildings, the lighting control systems deployed incorporates digitalization through motion sensors or wireless communication technology to operate or control the lighting’s operation and brightness. Such utilization often leads to low user satisfaction rates as energy minimalization is more optimized to reduce energy wastage without consideration of users’ lighting preferences. Although the presence of wireless controls allows users to manually adjust the lighting intensity based on their preferences, this becomes a problem when there is more than one occupant in the environment due differences in lighting preferences. Hence, the most preferrable lighting control system is one that can consider the lighting preferences of all the occupants in the room and to some extent, minimize the energy wastage for lighting. This study aims to propose a reinforcement learning (RL) control system that can obtain a balance between lighting preferences of occupants and energy efficiency within the environment. Through this control system, the RL agents will optimize the lighting comfort based on the lighting preferences profiles of all occupants within the environment while the negotiator will maximize the lighting comfort between different occupants with different lighting preferences while minimizing energy consumption. The control agents will be trained by Q-learning, which is a model-free reinforcement learning algorithm, and simulated with three different lighting preference profiles from three different occupants. To obtain the ideal learning parameter of the proposed control system, different learning parameters, such as ε – greedy value, max steps per episode, learning rate α, and discount rate γ, will be used to test the ideal learning conditions for this study. In addition, to test the adaptability of the proposed control system, changes will be made to the environment, such as a change in the number of occupants in the environment, different starting lighting state of the environment, and accounting for energy efficiency. From the results, the proposed control system was able to reach the optimum lighting comfort after 116 simulation runs. This shows that the proposed control system was able to achieve good lighting comfort optimization performance and has a relatively efficient learning speed even with the introduction of environmental changes. Bachelor of Engineering (Environmental Engineering) 2021-06-03T01:58:18Z 2021-06-03T01:58:18Z 2021 Final Year Project (FYP) Chan, K. J. N. (2021). Smart building with intelligent indoor lighting control system using reinforcement learning simulations. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150183 https://hdl.handle.net/10356/150183 en EN-41 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Environmental engineering
spellingShingle Engineering::Environmental engineering
Chan, Keno Jia Nuo
Smart building with intelligent indoor lighting control system using reinforcement learning simulations
description In most conventional indoor environments today, such as households or office buildings, the lighting control systems deployed incorporates digitalization through motion sensors or wireless communication technology to operate or control the lighting’s operation and brightness. Such utilization often leads to low user satisfaction rates as energy minimalization is more optimized to reduce energy wastage without consideration of users’ lighting preferences. Although the presence of wireless controls allows users to manually adjust the lighting intensity based on their preferences, this becomes a problem when there is more than one occupant in the environment due differences in lighting preferences. Hence, the most preferrable lighting control system is one that can consider the lighting preferences of all the occupants in the room and to some extent, minimize the energy wastage for lighting. This study aims to propose a reinforcement learning (RL) control system that can obtain a balance between lighting preferences of occupants and energy efficiency within the environment. Through this control system, the RL agents will optimize the lighting comfort based on the lighting preferences profiles of all occupants within the environment while the negotiator will maximize the lighting comfort between different occupants with different lighting preferences while minimizing energy consumption. The control agents will be trained by Q-learning, which is a model-free reinforcement learning algorithm, and simulated with three different lighting preference profiles from three different occupants. To obtain the ideal learning parameter of the proposed control system, different learning parameters, such as ε – greedy value, max steps per episode, learning rate α, and discount rate γ, will be used to test the ideal learning conditions for this study. In addition, to test the adaptability of the proposed control system, changes will be made to the environment, such as a change in the number of occupants in the environment, different starting lighting state of the environment, and accounting for energy efficiency. From the results, the proposed control system was able to reach the optimum lighting comfort after 116 simulation runs. This shows that the proposed control system was able to achieve good lighting comfort optimization performance and has a relatively efficient learning speed even with the introduction of environmental changes.
author2 En-Hua Yang
author_facet En-Hua Yang
Chan, Keno Jia Nuo
format Final Year Project
author Chan, Keno Jia Nuo
author_sort Chan, Keno Jia Nuo
title Smart building with intelligent indoor lighting control system using reinforcement learning simulations
title_short Smart building with intelligent indoor lighting control system using reinforcement learning simulations
title_full Smart building with intelligent indoor lighting control system using reinforcement learning simulations
title_fullStr Smart building with intelligent indoor lighting control system using reinforcement learning simulations
title_full_unstemmed Smart building with intelligent indoor lighting control system using reinforcement learning simulations
title_sort smart building with intelligent indoor lighting control system using reinforcement learning simulations
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150183
_version_ 1702431300209606656