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A data-driven workflow to improve energy efficient operation of commercial buildings: A review with real-world examples:

A data-driven workflow to improve energy efficient operation of commercial buildings: A review... Data-driven building operation and maintenance research such as metadata inference, fault detection and diagnosis, occupant-centric controls (OCCs), and non-invasive load monitoring have emerged (NILM) as independent domains of study. However, there are strong dependencies between these domains; for example, quality of metadata affects the usability of fault detection and diagnostics techniques. Further, faults in controls hardware and programs limit the performance of OCCs. To this end, a literature review was conducted to identify the dependencies between these domains of research. Additionally, real-world examples using operational data from three institutional buildings in Ottawa, Canada, were provided and discussed to demonstrate these dependencies. Finally, a holistic tool-agnostic workflow was introduced which suggested the implementation of operational energy efficiency measures in the following order to ensure their full potential: (1) improve metadata, (2) address faults, (3) implement OCCs, and (4) monitor enhanced key performance indicators (KPIs). The proposed workflow is intended to be comprehensive, reproducible, nonintrusive, and inexpensive to implement. Practical applications: Optimization of building operations has been emerging among energy management professionals as a relatively low-cost means to achieve energy efficiency and minimize occupants’ discomfort. To this end, this study introduces a tool-agnostic data-driven workflow to building energy management practitioners that can assist them in achieving increased energy efficiency. The proposed workflow recognizes the interdependency of the various domains of research which have historically been treated independently. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Building Services Engineering Research and Technology: An International Journal SAGE

A data-driven workflow to improve energy efficient operation of commercial buildings: A review with real-world examples:

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References (108)

Publisher
SAGE
Copyright
Copyright © 2022 by SAGE Publications
ISSN
0143-6244
eISSN
1477-0849
DOI
10.1177/01436244211069655
Publisher site
See Article on Publisher Site

Abstract

Data-driven building operation and maintenance research such as metadata inference, fault detection and diagnosis, occupant-centric controls (OCCs), and non-invasive load monitoring have emerged (NILM) as independent domains of study. However, there are strong dependencies between these domains; for example, quality of metadata affects the usability of fault detection and diagnostics techniques. Further, faults in controls hardware and programs limit the performance of OCCs. To this end, a literature review was conducted to identify the dependencies between these domains of research. Additionally, real-world examples using operational data from three institutional buildings in Ottawa, Canada, were provided and discussed to demonstrate these dependencies. Finally, a holistic tool-agnostic workflow was introduced which suggested the implementation of operational energy efficiency measures in the following order to ensure their full potential: (1) improve metadata, (2) address faults, (3) implement OCCs, and (4) monitor enhanced key performance indicators (KPIs). The proposed workflow is intended to be comprehensive, reproducible, nonintrusive, and inexpensive to implement. Practical applications: Optimization of building operations has been emerging among energy management professionals as a relatively low-cost means to achieve energy efficiency and minimize occupants’ discomfort. To this end, this study introduces a tool-agnostic data-driven workflow to building energy management practitioners that can assist them in achieving increased energy efficiency. The proposed workflow recognizes the interdependency of the various domains of research which have historically been treated independently.

Journal

Building Services Engineering Research and Technology: An International JournalSAGE

Published: Mar 14, 2022

Keywords: Data-driven; energy flow; energy efficiency; fault detection; key performance indicators; metadata; workflow

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