Building fault detection data to aid diagnostic algorithm creation and performance testing

Granderson, Jessica; Lin, Guanjing; Harding, Ari; Im, Piljae; Chen, Yan

doi:10.1038/s41597-020-0398-6

Cited by 60 publications

(15 citation statements)

References 16 publications

(10 reference statements)

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“…For grey-box and data-driven approaches, such a comprehensive dataset is critical for training reliable models. As of now, there are numerous efforts in either collecting data from measurements 13 – 16 or synthesizing data with simulations 17 , 18 . However, each of the dataset has its strengths and limitations.…”

Section: Background and Summarymentioning

confidence: 99%

A synthetic building operation dataset

Wang

Hong

2021

Sci Data

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This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

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Section: Background and Summarymentioning

confidence: 99%

A synthetic building operation dataset

Wang

Hong

2021

Sci Data

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“…While it is well known that Haystack and Brick are used by energy management information systems (EMIS) to support energy analysis and fault detection and diagnostics applications (FDD) [28,37], documentation for the implementation of algorithms using standardized concepts defined by either schema has not been demonstrated. Although detailed descriptions of control and FDD algorithms are provided in many previous publications [38][39][40][41][42], they merely include point name descriptions and don't reference any data schema objects, which we believe is a serious opportunity missed and a novelty of the research performed by our study. Furthermore, relying on occupancy counts for occupant-centric KPI calculations can substantially limit the number of buildings for which the KPIs can be calculated, since most buildings today can't capture this information.…”

Section: Point Of Departurementioning

confidence: 99%

Development and Application of Schema Based Occupant-Centric Building Performance Metrics

2021

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Occupant behavior can significantly influence the operation and performance of buildings. Many occupant-centric key performance indicators (KPIs) rely on having accurate counts of the number of occupants in a building, which is very different to how occupancy information is currently collected in the majority of buildings today. To address this gap, the authors develop a standardized methodology for the calculation of percent space utilization for buildings, which is formulated with respect to two prevalent operational data schemas: the Brick Schema and Project Haystack. The methodology is scalable across different levels of spatial granularity and irrespective of sensor placement. Moreover, the methods are intended to make use of typical occupancy sensors that capture presence level occupancy and not counts of people. Since occupant-hours is a preferable metric to use in KPI calculations, a method to convert between percent space utilization and occupant-hours using the design occupancy for a space is also developed. The methodology is demonstrated on a small commercial office space in Boulder, Colorado using data collected between June 2018 and February 2019. A multiple linear regression is performed that shows strong evidence for a relationship between building energy consumption and percent space utilization.

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“…The data are available for the period of one year, which have been used to track human-building interaction 32 . Building fault detection data 33 have recently been made available to benchmark the performance of fault detection and diagnostic algorithms.…”

Section: Background and Summarymentioning

confidence: 99%

CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets

et al. 2020

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this paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m 2 office building located in Bangkok, thailand. the electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (°C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple aC units.

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Building fault detection data to aid diagnostic algorithm creation and performance testing

Cited by 60 publications

References 16 publications

A synthetic building operation dataset

A synthetic building operation dataset

Development and Application of Schema Based Occupant-Centric Building Performance Metrics

CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets

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