My thesis work is focused on advancing the state of the art in smart building monitoring and control problems. A major part of my thesis will develop combined model- and data-driven models for the energy systems of buildings and apply them for energy monitoring and optimization, fault diagnosis, and fault-adaptive control. In past work, I have already developed initial machine learning-based models for energy optimization. In this paper, I am focusing on developing a framework using bond graphs to build a model of the HVAC system and understand how different factors affect the measurement variables of this system. This information will help us do subsequent fault diagnosis by studying the characteristics of the system behavior represented by the temporal causal graph. It will also provide the basis for fault-adaptive control of buildings
How to Cite
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.