Smart meter data-driven building energy efficiency assessment and management
Project Description

This project aims to compare different machine-learning/deep-learning methods to evaluate building energy efficiency assessment in support of sustainable energy use management. The comparison of various data-driven methods will validate the consistency of assessment criteria learnt from these methods to determine building energy efficiency. This project is a continuous research task based on our past exploration of data-driven studies on energy efficiency assessment.

Supervisor
LU Zhongming
Co-Supervisor
LU Zhongming
Quota
2
Course type
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

The applicants will help multi-source data cleaning and algorithm development to answer the proposed project question. Some experience in programing is preferred.

Applicant's Learning Objectives

To improve the understanding of scientific research methods
To improve the capability of using data science to solve sustainability challenges
To enhance transdisciplinary research at the interface of sustainability and data science

Complexity of the project
Moderate