Sensing and Data Acquisition

Dong, Bing; Kjærgaard, Mikkel Baun; Simone, Marilena De; Gunay, H. Burak; O’Brien, William; Mora, Dafni; Dziedzic, Jakub; Zhao, Jie

doi:10.1007/978-3-319-61464-9_4

Cited by 20 publications

(15 citation statements)

References 40 publications

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“…Relevant occupant-centric data can be generated by sensors, control points, system logs, or manual observations, as covered by (33). To collect occupantcentric data, many different types of sensors and data sources are available.…”

Section: Data Collection (1)mentioning

confidence: 99%

“…There are some risks specific to occupant behavior research that makes the ethics and data collection more challenging (33). Eventually, these could be an obvious obstacle for any occupant-centric open data release.…”

Section: Ethical Considerationsmentioning

confidence: 99%

“…• Video data collection: Surveillance video data shows potential significant ethical risks (33). For instance, the employee may feel uncomfortable and less productive for fear of their undesirable behaviors are recorded (e.g., lack of presence).…”

Section: Ethical Considerationsmentioning

confidence: 99%

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Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Kjærgaard

Ardakanian

Carlucci

et al. 2020

Building and Environment

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Section: Data Collection (1)mentioning

confidence: 99%

Section: Ethical Considerationsmentioning

confidence: 99%

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Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Kjærgaard

Ardakanian

Carlucci

et al. 2020

Building and Environment

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“…For the training of the supervised methods, the long-term continuous and costly ground truth collection of the actual occupancy is needed. This has been detected as one of the challenges of occupancy prediction technologies by Dong et al [25,26] as well. Following-on this, the unsupervised data mining method of consumption data to find daily occupancy level is evaluated.…”

Section: Introductionmentioning

confidence: 99%

From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach

2020

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Climate change and technological development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters, and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence that would offer new insights to the building’s operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.

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“…The existing literature mainly focuses on one or few stakeholders for energy flexible buildings, e.g. occupants (Dong et al 2018) or the technical aspects, e.g. VPPs (virtual power plants) (Clausen et al 2016).…”

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confidence: 99%

A discussion of building automation and stakeholder engagement for the readiness of energy flexible buildings

Jôrgensen

2018

Energy Inform

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Building automation enables the possibility of energy flexibility in buildings. To investigate the motivation and barriers for the energy flexibility in buildings, this study develops a conceptual framework of the readiness for energy flexible buildings by conducting interviews with building automation suppliers, electricity supplier, district heating supplier, distribution system operator, energy service companies, experts in energy and buildings, building managers, and occupants. The two main parts of the framework are building preparation, grid and market conditions following the impacts of regulation and policies, stakeholder collaboration and integrated building automation. A case study of campus buildings is conducted to demonstrate the framework. The result of the case study shows that the main barriers for buildings to provide energy flexibility are 1) many buildings are too old and need to be refurbished, 2) the benefit of providing energy flexibility to the grid is not sufficient, 3) building management systems need to be either installed or upgraded to response to the demand from the grid. Building managers believe that buildings can provide energy flexibility by building automation and distributed energy resources, but they consider energy efficiency to be more important than providing flexibility to the grid. Meanwhile, occupants have different opinions regarding the comfort level of indoor air quality and control, and the differences are based on various factors, e.g. location, room type, and building ages. Background 'The energy flexibility of a building is the ability to manage its energy demand and generation according to local climate conditions, user needs and grid requirements' (Jensen et al. 2017a). A large part of the energy use of buildings may be shifted in time, and significantly contribute to increasing flexibility in the energy system (Jensen et al. 2017b). Buildings can provide flexibility to the energy system in various ways, e.g. load shifting and HVAC (heating, ventilation, and air conditioning) control, and participate in two types of DR (demand response) programs: explicit and implicit demand response. Implicit DR assumes that electricity consumers may alter their electricity consumption due to a price signal that is provided at a different time (Mandatova and Lorenz 2013). Meanwhile, explicit DR is divided into traditional-based (e.g. direct load control, interruptible pricing) and market-based (e.g. emergency demand response programs and ancillary services) (Sebastian and Margaret 2016).

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Sensing and Data Acquisition

Cited by 20 publications

References 40 publications

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach

A discussion of building automation and stakeholder engagement for the readiness of energy flexible buildings

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