Synthesis of Year Two Outcomes in the Smart Energy Analytics Campaign

Kramer, Hannah; Lin, Guanjing; Granderson, Jessica; Curtin, Claire; Crowe, Eliot

doi:10.2172/1545159

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…It is therefore necessary to analyse the market delivery and find a place for detection tools with the highest value added. A good example of this is the Smart Energy Analytics Campaign [24] which focused on improving the use of practical diagnostics and the publication of many useful documents on the commercial aspects of this usage.…”

Section: Business Aspectsmentioning

confidence: 99%

Versatile AHU fault detection – Design, field validation and practical application

Nehasil¹,

Dobiášová²,

Mazanec³

et al. 2021

Energy and Buildings

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Section: Business Aspectsmentioning

confidence: 99%

Versatile AHU fault detection – Design, field validation and practical application

Nehasil¹,

Dobiášová²,

Mazanec³

et al. 2021

Energy and Buildings

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“…EMIS drives direct value to agencies through reducing energy and demand costs and preventing building performance from drifting over time. These savings have been cited as the highest motivators for installing EMIS (Kramer et al 2019a), and there is great potential across all federal agencies, where total utility expenditures topped $16.5 billion in Fiscal Year (FY) 2018 (DOE 2021a). If all agencies adopted EMIS and realized the median savings of 9% reported by the Smart Energy Analytics Campaign (SEAC, a public-private partnership encouraging the use of EMIS technologies monitoring practices), the annual energy cost savings would be approximately $1.5 billion (Kramer et al).…”

Section: Direct Valuementioning

confidence: 99%

Energy Management Information Systems Technical Resources Report

Dean

James

2021

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All reported findings are based on vendor-supplied information at the time of the study. Current capabilities are subject to change, and readers are encouraged to confirm information based on their specific needs. Moreover, the Energy Management Information Systems that were selected as examples are representative of the market, but not comprehensive, and inclusion in the study does not imply endorsement.

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“…EMISs belong to the rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, often leveraging advanced data analytics-based technologies. According to [9], the first classification of EMISs distinguishes such systems considering if their functionalities are enabled at the meter or system-level. The first category of EMISs considers data measurements at a high level (e.g., data related to the total load or of the main sub-loads) while system-level EMISs are focused on more detailed data related to the operation of specific systems or components.…”

Section: Introductionmentioning

confidence: 99%

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

2021

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Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus.

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Synthesis of Year Two Outcomes in the Smart Energy Analytics Campaign

Cited by 7 publications

References 5 publications

Versatile AHU fault detection – Design, field validation and practical application

Versatile AHU fault detection – Design, field validation and practical application

Energy Management Information Systems Technical Resources Report

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

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