Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/222620
Title: RECOMMENDATION SYSTEMS FOR THE BUILT ENVIRONMENT: PREDICTING AND MANAGING THERMAL COMFORT & ENERGY EFFICIENCY
Authors: NG QI XUAN MADELINE
Keywords: 2020-2021
Building
Bachelor's
BACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT)
Clayton Miller
Artificial Intelligence
Built Environment
Energy Efficiency
HVAC
Machine Learning
Predicting
Recommendation Systems
Research Subject Categories::TECHNOLOGY::Civil engineering and architecture::Building engineering
Thermal Comfort
Issue Date: 11-May-2021
Citation: NG QI XUAN MADELINE (2021-05-11). RECOMMENDATION SYSTEMS FOR THE BUILT ENVIRONMENT: PREDICTING AND MANAGING THERMAL COMFORT & ENERGY EFFICIENCY. ScholarBank@NUS Repository.
Abstract: Recommendation systems are a form of Artificial Intelligence and Machine Learning (AI/ML) that are able to learn the preferences of users or pre-emptively guess the preferences of new users who have never used the application before and recommend an item or a service to the users. AI/ML are increasingly commonplace tools that are able to benefit users of applications that they are implemented in, ranging from e-commerce to video and music entertainment services. This paper investigates the possibilities of applying AI/ML recommendation systems to the context of the built environment and the advantages that would occur from such implementation, focusing on determining and improving the thermal comfort of occupants in a building and estimating the improved energy efficiency in a building when there is a higher overall thermal comfort for occupants. When applied to a dataset consisting of 30 participants and benchmarked against the most commonly used method of evaluating thermal comfort (PMV/PPD method), there is evidence that recommendation systems assisted by AI/ML were able to accurately determine the thermal comfort levels of occupants by at least 50% or more on average. Recommendation systems were also able to determine the change in thermal environment required (prefer warmer, no change, prefer cooler) to achieve thermal comfort. These results illustrate that with further refinement, there is great feasibility for recommendation systems in the built environment for the purpose of increasing the comfort of occupants. A discussion is provided about the potential increase of thermal comfort and energy efficiency from implementing recommendation systems in buildings, an outlook on other practical uses other than thermal comfort, and future research that could be done to refine the study of recommendation systems for the built environment.
URI: https://scholarbank.nus.edu.sg/handle/10635/222620
Appears in Collections:Bachelor's Theses

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Ng Qi Xuan Madeline 2020-2021 Dissertation FINAL.pdfDissertation Submission 2021858.26 kBAdobe PDF

RESTRICTED

NoneLog In

Page view(s)

66
checked on Feb 2, 2023

Download(s)

10
checked on Feb 2, 2023

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.