A review of data collection and analysis requirements for certified green buildings

Afroz, Zakia; Gunay, H. Burak; O’Brien, William

doi:10.1016/j.enbuild.2020.110367

Cited by 28 publications

(12 citation statements)

References 44 publications

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“…81 Other building certifications such as BREEAM, BOMA Best 3, Green Globes EB, and Well v2 promote operational data collection by specifying it as a prerequisite or by granting credits for implementation. 82 However, these requirements do not apply to existing buildings (i.e., the majority of the building stock), which continue to rely largely on bulk metering of energy resulting in missed opportunities to address operational issues that might affect energy use and occupants' comfort.…”

Section: Monitoringmentioning

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: Monitoringmentioning

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

View full text Add to dashboard Cite

show abstract

“…Rating systems have traditionally responded to these factors with flexibility, assigning these criteria to optional credits, instead of prerequisites. A few systems require demanding performance verification in operation, yet even in these cases established protocols are often sporadic (Afroz et al 2020). For example, WELL v2 and BEAM Plus (Hong Kong) require occasional performance testing once every three and five years, respectively (WELL 2021).…”

Section: Performance Measurementmentioning

confidence: 99%

“…New players on the market increasingly create their own monitoring programmes and select elements that suit them best. This creates discrepancies in protocols and data infrastructure requirements across rating systems that limit comparability (Afroz et al 2020) and the creation of benchmarks.…”

Section: Performance Measurementmentioning

confidence: 99%

The future of IEQ in green building certifications

Licina

Wargocki

Pyke³

et al. 2021

Buildings and Cities

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“…However, errors, including missing and incorrect values, can still be made by humans and machines. Thus, investing in data infrastructures such as sensors, meters, sub-meters and data archiving systems and linking these to data-driven building operation applications are still essential [30].…”

Section: Collecting Energy Performance Datamentioning

confidence: 99%

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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A review of data collection and analysis requirements for certified green buildings

Abstract: Highlights The review of literature highlights performance gap in certified buildings. The presented comparative analysis shows differences across certification systems. This survey shows major discrepancies in data infrastructure requirements.

Cited by 28 publications

References 44 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

The future of IEQ in green building certifications

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

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