Disaggregation of commercial building end-uses with automation system data

Gunay, H. Burak; Shi, Zixiao; Wilton, Ian; Bursill, Jayson

doi:10.1016/j.enbuild.2020.110222

Cited by 19 publications

(10 citation statements)

References 24 publications

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“…25,26 Tracking and understanding energy end-uses in buildings is fundamental to energy management processes and ultimately achieving performance targets. 28 However, installing submetering infrastructure in existing buildings is still considered a barrier due to the associated costs and feasibility.…”

Section: The Proposed Workflowmentioning

confidence: 99%

“…Load disaggregation using operational data from the BAS offers a low-cost and nonintrusive opportunistic tool for obtaining the benefits of submetering that includes understanding energy flows and monitoring energy performance in buildings. 27,28 Load disaggregation can support energy management professionals in making informed operational decisions and consequently improve energy performance of buildings.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: The Proposed Workflowmentioning

confidence: 99%

Section: Introductionmentioning

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

“…A study showed that detailed feedback information from monitoring systems could be provided for users, which can encourage energy-saving behavior, improve energy management, and upgrade fault diagnosis [5]. Buildings can achieve more than 10% of potential carbon savings by obtaining details on energy consumption, indicating its importance in energy management [6].…”

Section: Introductionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Ling¹,

Tao²,

Zheng³

et al. 2021

Buildings

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Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.

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“…For example, combining energy meter data with HVAC controls network data can be used disaggregate end-uses and identify faulty equipment. Gunay et al [11] employed linear regression models using HVAC controls data to characterize the operational state of major energy systems, which enabled high accuracy disaggregation of end-uses at low temporal resolution meter data. Disaggregation produced an accurate representation of the specific components' power draw and the insights generated can be used to focus fault detection efforts into specific equipment rather than an entire system.…”

Section: Data-driven Approaches To Building Energy Managementmentioning

confidence: 99%

“…These approaches are software based and can be implemented in a wide variety of existing buildings [9]. However, despite its prevalence in academia and demonstrated benefits of multi-source analytics [10], [11], such methods to improve energy efficiency remain disparate. Furthermore, they are not widely implemented in practice, unlike methods intended for the design phase.…”

Section: Introductionmentioning

confidence: 99%

Development, Implementation, and Industry Reception of a Novel Multi-source, Data-driven Building Energy Management Toolkit

Markus¹

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Building energy performance is often negatively impacted by inefficient and uninformed operations, leading to wide disparities between predicted and actual energy use in commercial and institutional buildings. Though ample research in data-driven building operations and maintenance analytics has derived various methodologies for extracting energy-saving insights that can supplement operating practice, these approaches have traditionally remained disparate, limiting their application, and are exclusively prevalent in academia. Furthermore, operations personnel who regularly manage controls and maintenance of HVAC equipment can benefit from these novel approaches in augmenting their duties and optimizing building energy efficiency. This research explores the development of a novel multi-source, data-driven building energy management toolkit as a synthesis of established data-driven approaches in the literature comprising inverse energy modelling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods. The toolkit inputs various data types to detect hard and soft faults, optimize sequences of operation settings, and monitor energy flows, occupancy patterns, and occupant satisfaction. The toolkit's unique multi-source analytical approach was used to pinpoint operational deficiencies stemming from inappropriate zone temperature overheating thresholds and perimeter heating devices. Energy-saving insights were generated using data from four separate case study buildings to demonstrate the utility of the toolkit's web-based application platform. Finally, interviews with building operators and facility managers to their interpretations of insights from data-driven approaches were conducted; possible barriers were identified which inhibited industry professionals from effectively deriving and utilizing insights from the visualizations and KPIs.

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Disaggregation of commercial building end-uses with automation system data

Cited by 19 publications

References 24 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

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Development, Implementation, and Industry Reception of a Novel Multi-source, Data-driven Building Energy Management Toolkit

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