Challenges in the Data Collection for Diagnostics of Smart Buildings

Lazarova-Molnar, Sanja; Mohamed, Nader

doi:10.1007/978-981-10-0557-2_90

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…31 Most point labeling in the BASs of existing buildings do not follow any standards and are dictated by control vendors or technicians, which results in distinctly variable labels. 25,32,33 For example, Table 2 demonstrates examples of inconsistent name tags used to describe the same items in different BASs, or in some cases, the same BAS, from the three different institutional buildings considered in this study. Beyond the inconsistency in naming conventions for individual tags, the structure of the data labels as a whole can vary between similar data points.…”

Section: Metadatamentioning

confidence: 99%

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

Abuimara

Hobson

Gunay

et al. 2022

Building Services Engineering Research and Technology

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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.

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Section: Metadatamentioning

confidence: 99%

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

Abuimara

Hobson

Gunay

et al. 2022

Building Services Engineering Research and Technology

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“…Actually, meters and sensors facilitate collection of large amounts of data that is utilized for different purposes, such as fault discovery and diagnosis. 12…”

Section: Background and Related Workmentioning

confidence: 99%

Collaborative data-driven reliability analysis of multi-state fault trees

Niloofar

Lazarova-Molnar

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Fault tree modeling and failure analysis of systems that are equipped with sensors and meters are becoming more automated and less human-dependent. For a single system to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build representative models to increase its reliability. Therefore, if multiple systems with similar functionalities cooperate, the resolution of the collected data will increase. This leads to extracting fault trees with higher accuracy in failure detection and prediction. In this paper, we present an extended approach for collaborative Data-Driven Fault Tree Analysis (DDFTA) of a system which extracts repairable fault trees from time series data streaming from multiple systems/machines sharing similar functionalities. Results are analyzed to estimate the system’s reliability measures and investigate the effect of number of machines cooperating in data collection. Our method is not limited to binary (two states) components, nor to exponential distributions. Results show that applying collaborative data analytics significantly increases the accuracy of data-driven fault tree analysis, specifically for systems following nonexponential distributions.

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“…The main problem is that this kind of data cannot be collected in a non-intrusive fashion, and it is very rarely kept track of. Lazarova-Molnar et al in [2] thoroughly describe this problem and identify the main challenges, as the following:…”

Section: Challenges In the Data Collection For Fddmentioning

confidence: 99%

“…FDD in buildings can be data-driven, meaning that data collected, for example from the sensors within the building, is used for performing the detection and diagnostics. One of the challenges for data-driven FDD in buildings can be the lack of historical data available [2], and this is especially the case for newly built buildings like OU44. The data from the OU44's range of sensors is already being used for FDD, and as of now, there is no event logging in place.…”

Section: Crowdsourcing Mobile App To Support Data Collection For Fddmentioning

confidence: 99%

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Mobile crowdsourcing of occupant feedback in smart buildings

Lazarova-Molnar

Logason

Andersen

et al. 2017

SIGAPP Appl. Comput. Rev.

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Energy consumption of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas include rigorous measures aimed at reducing the energy consumption and, thereby, the carbon footprint. Timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) have the potential to reduce energy consumption cost by approximately 15--30%. Most FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data about faults and related events. Based on our experience, such data is very sparse and inadequate, mostly because of the difficulty and lack of incentive to collect such data in a structured manner. In this article we introduce the idea of using crowdsourcing to support FDD-related data collection, and illustrate the concept through a mobile application that has been implemented for this purpose. Furthermore, we describe our experience from using the mobile application in a university building and propose a strategy of how to successfully deploy the application in new buildings.

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Challenges in the Data Collection for Diagnostics of Smart Buildings

Cited by 11 publications

References 22 publications

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 with real-world examples

Collaborative data-driven reliability analysis of multi-state fault trees

Mobile crowdsourcing of occupant feedback in smart buildings

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