Development and Demonstration of Surrogate Models to Predict Energy-Related Building Features from Heating and Cooling Load Signature

This thesis introduces an innovative, efficient, and cost-effective approach for screening office buildings to identify the buildings with the most retrofit potential. Traditional auditing methodologies, including advanced inverse-based methods, often face constraints in terms of engineering cost, time, high fidelity operational data availability, and scalability. Utilizing inverse model-based machine learning, surrogate models for 12 mid to high-rise buildings were developed to predict energy-related features from widely available heating and cooling load data. The research explores two methodologies: 1) unsupervised learning with clustering to group energy-related features, and 2) supervised learning using artificial neural networks (ANNs) to predict these features. Both methods utilize simulated data and three-parameter change point models (3P CPMs) for efficient characterization of energy performance. The results show reasonable accuracy in predictions, with clustering addressing multicollinearity, offering a scalable alternative to traditional audits. A user-friendly software tool illustrating the real-world application of the surrogate model is also introduced.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Building

Grantor dc:publisher

Carleton University

Year dc:date.issued

2023

Author dc:creator

• Ferreira, Shane

Statement dc:rights

• Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2023-15797

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/41820