Occupancy based demand response HVAC control strategy

Erickson, Varick L.; Cerpa, Alberto E.

doi:10.1145/1878431.1878434

Cited by 157 publications

(89 citation statements)

References 3 publications

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“…Bode project develops a system that tracks user movement in building spaces using a camera network solution called SCOPES (a distributed smart cameras object position estimation system) [52,53]. Bode aims to develop a framework that estimates and predicts user occupation of building spaces.…”

Section: The Office Sectormentioning

confidence: 99%

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Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

605

297

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a b s t r a c tOccupant presence and behaviour in buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energyunaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity.

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Section: The Office Sectormentioning

confidence: 99%

“…Erickson et al construct several models in [52,54], and [53] for predicting user mobility patterns in buildings. Using these models, they can predict room usage, thereby enabling to control the HVAC systems in an adaptive manner.…”

Section: Prediction Of Occupancy Patternsmentioning

confidence: 99%

Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

605

297

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“…However, it has been found that average building occupancy for commercial buildings is at most a third of its maximum designed-for occupancy, even among office spaces at their peak working hours [64]. In this regards, occupancy-sensing data provides significant information for building management systems to adapt their system-e.g., HVAC and lighting-according to the exact number of occupants in a building at a given time [65][66][67]. The current status of sensing technologies therefore provides opportunities to economically monitor individual occupants and their energy consumption [68,69].…”

Section: Figurementioning

confidence: 99%

“…Furthermore, in addition to traditional software, traditional building management systems also have limitations with real-time inputs of occupancy-related dynamic factors, such as the number of occupants and their preferences, actions, and decisions [63]. This limitation is problematic since the inputs of real-time occupancy information can reduce HVAC and lighting energy consumption by up to 20 and 30 percent, respectively [56,66,67,75]. In response to these limitations in modeling occupants' energy-use behaviors, a number of studies have recently worked on various simulation techniques to attempt to overcome these particular limitations.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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“…Indoor location information is valuable to the building industry, and it lays the basis of a wide range of applications, such as locating building components [1], indoor navigation for the visually impaired [2,3], automated lighting and air conditioning control for building energy conservation [4,5], and emergency response support with space-specific information [6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of WSN and RFID Technologies for Real-Time Occupancy Information

Çalış¹,

Deora²,

Li³

et al. 2011

28th International Symposium on Automation and Robotics in Construction (ISARC 2011)

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Real-time occupancy information is valuable to the building industry for a wide variety of applications such as on-site safety management, energy conservation, emergency response and so on. With the emergence of various technologies that can potentially be used for indoor location sensing (ILS), the building industry is in search for an ILS solution to provide accurate and cost efficient occupancy information. This study assesses two ILS systems that are built on two promising modalities: radio frequency identification (RFID) and wireless sensor networks (WSNs). Two algorithms were proposed and tested in a computer lab with an area of 235 m 2 . Six occupants, who were attached with both RFID tags and WSN nodes, were tracked simultaneously. The occupants remained seated throughout the test, and the occupancy was monitored. The results show that the proposed systems were able to provide accurate room level occupancy information majority of the time, and that the choice of algorithm and technology affected the uniformity of the results.

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Occupancy based demand response HVAC control strategy

Cited by 157 publications

References 3 publications

Energy intelligent buildings based on user activity: A survey

Energy intelligent buildings based on user activity: A survey

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Assessment of WSN and RFID Technologies for Real-Time Occupancy Information

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