Linking building energy consumption with occupants’ energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)

Rafsanjani, Hamed Nabizadeh; Ahn, Changbum R.; Chen, Jiayu

doi:10.1016/j.enbuild.2018.05.007

Cited by 55 publications

(43 citation statements)

References 26 publications

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“…Other companies, such as ONZO Ltd. or Bidgely, Inc., propose similar approaches, most of them based on a smart meter/sensor and machine learning for energy disaggregation. With regard to the drawbacks presented by the commercial solutions, it is worth noting that most of them are constrained to low sampling rates, 1 Hz maximum [9,10], thus limiting the achieved performance and the chance to use them in some demanding types of applications. Even worse, sometimes this sampling frequency is not consistent over time, thus adding a new challenge.…”

Section: Data Collectionmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: Data Collectionmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…There is a wide consensus in academia that there is a need of further research on occupant behavior, with a particular gap being the context of commercial buildings [10]. Building occupants have a significant influence on unregulated loads, such as plug loads [4,5], which if not properly managed, can negatively impact energy conservation program efforts. In literature reviews by Hong et al [11] and Zhang et al [8], the behavior-related potential energy savings in commercial buildings was found to be in a range of 5-30%.…”

Section: Occupant Behavior In Commercial Buildingsmentioning

confidence: 99%

“…While there is less academic dialogue on the influence of occupant behavior on energy consumption in commercial buildings, the discussion is growing [11]. For instance, in exploring strategies to reduce occupant-controlled plug-loads in commercial buildings, Rafsanjani et al [5] developed non-intrusive load-monitoring techniques to link power changes to occupant events over time. Staats et al [22] recorded a 6% reduction in gas consumption in a university office building following an information intervention.…”

Section: Occupant Behavior In Commercial Buildingsmentioning

confidence: 99%

“…In the United States, residential and commercial buildings account for 40% of national energy consumption [3]. Over 20% of national consumption is attributable to commercial buildings, which also show the highest energy intensity and growth rates [5]. With pressures of climate change and sustainability goals, there have been numerous efforts to improve building energy efficiency via technological modifications.…”

Section: Introductionmentioning

confidence: 99%

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Occupant Behavior for Energy Conservation in Commercial Buildings: Lessons Learned from Competition at the Oak Ridge National Laboratory

et al. 2019

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Accompanying efforts worldwide to deploy sustainable building technologies shows a pressing need for expanded research on occupant behavior. Discourse is lacking concerning drivers of occupant behavior for energy conservation, especially in the case of commercial buildings. This paper explores potential determinants of occupant behavior for energy conservation in commercial buildings. This is investigated in a case study of a two-month energy conservation competition involving eight office buildings at the Oak Ridge National Laboratory. Four buildings achieved energy savings based on the previous year’s baseline. Potential challenges and success factors of occupant behavior for energy conservation during the competition were explored based on an explanatory research design incorporating energy data, participant interviews, and surveys. The findings suggest that both social and technological aspects may be important drivers of energy conservation. The determinants of occupant behavior for energy conservation in commercial buildings suggested for further research include bottom-up involvement, stakeholder relationship management, targeted information, real-time energy visualization, and mobile social platforms. This paper presents initial implications, with a need for further research on these propositions and on their impacts on occupant behavior. This paper aims to contribute to both academia and practitioners in the arena of commercial building sustainability.

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“…Recent research [2][3][4][5][6][7][8][9] shows that energy conservation in office buildings require a detailed understanding of occupants' energy-use patterns/behaviors and intervening in these patterns/behaviors contributes to the energy savings up to 30 percent in such buildings [10]. In this context, it has been indicated that due to the direct control of occupants on miscellaneous electric loads (MELs) [11,12], adopting energy conservation behaviors among occupants could greatly reduce these loads energy consumption [10,13]; MELs represent more than 35 percent of energy consumption in office buildings [14]. Therefore, a correct monitoring of energy-use patterns is critical to achieve the ultimate goal of optimizing building energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

Rafsanjani

Ghahramani

2019

Journal of Building Engineering

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Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants' energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants' energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92 percent of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants' energyuse patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants' energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure.

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Linking building energy consumption with occupants’ energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)

Cited by 55 publications

References 26 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Occupant Behavior for Energy Conservation in Commercial Buildings: Lessons Learned from Competition at the Oak Ridge National Laboratory

Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings

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