25 research outputs found

    Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

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    Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior. 

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

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    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 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

    A Global Building Occupant Behavior Database

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    This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting

    Virtual testing of advanced composites, cellular materials and biomaterials: A review

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    This paper documents the emergence of virtual testing frameworks for prediction of the constitutive responses of engineering materials. A detailed study is presented, of the philosophy underpinning virtual testing schemes: highlighting the structure, challenges and opportunities posed by a virtual testing strategy compared with traditional laboratory experiments. The virtual testing process has been discussed from atomistic to macrostructural length scales of analyses. Several implementations of virtual testing frameworks for diverse categories of materials are also presented, with particular emphasis on composites, cellular materials and biomaterials (collectively described as heterogeneous systems, in this context). The robustness of virtual frameworks for prediction of the constitutive behaviour of these materials is discussed. The paper also considers the current thinking on developing virtual laboratories in relation to availability of computational resources as well as the development of multi-scale material model algorithms. In conclusion, the paper highlights the challenges facing developments of future virtual testing frameworks. This review represents a comprehensive documentation of the state of knowledge on virtual testing from microscale to macroscale length scales for heterogeneous materials across constitutive responses from elastic to damage regimes

    Non-intrusive Occupant Load Monitoring in Commercial Buildings

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    Commercial buildings consume more than 20 percent of total energy use in the United States and they have the highest energy-use intensity and growth rate compared to other major sectors. Promoting energy-saving behaviors among occupants has recently been considered as the most cost-effective approach for reducing commercial building energy consumption, especially for reducing energy consumption of miscellaneous electric loads (MELs) due to direct control of occupants over MELs. Therefore, tracking MELs consumption and linking it with occupants’ energy-saving behavior is critical in intervening occupants’ energy-use behaviors. Currently, individual plug-load meters at an individual’s workspace are mainly used for tracking MELs in a commercial building. However, the implementation of this approach for full scale adoption requires a large initial investment on the part of the business. In addition, such an approach cannot assess occupants’ use on shared resources (e.g., lighting, shared office electronics). On the other hand, non-intrusive load-monitoring is considered a cost-effective and feasible tool to disaggregate building-level data for estimating appliance-specific energy consumption. Previous studies have suggested that adding occupancy sensing data to a load disaggregation process can help in economically estimating occupant-specific energy consumption. However, there is still a gap in properly linking appliance-specific energy consumption to occupants’ energy-use behaviors in commercial buildings. In response, this dissertation proposes an approach which tracks occupant-specific energy-use right after they enter to a building (entry event) and right before they leave a building (departure event); occupants’ behaviors at these events have a large impact on a building’s energy consumption. By utilizing density-based clustering and discriminant analysis, the approach couples occupancy information collected from Wi-Fi infrastructures with aggregated energy-load data to disaggregate load data down to the level of individual occupants. This critically helps understanding individual occupants’ energy-use behaviors in an economic manner and particularly allows to deliver tailored information through personalized feedback to an occupant who follows non-energy-saving behavior, to modify her energy actions to energy efficient behavior

    Energy and Occupancy Data by SSC Group

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    Electric energy data

    Non-intrusive Occupant Load Monitoring in Commercial Buildings

    No full text
    Commercial buildings consume more than 20 percent of total energy use in the United States and they have the highest energy-use intensity and growth rate compared to other major sectors. Promoting energy-saving behaviors among occupants has recently been considered as the most cost-effective approach for reducing commercial building energy consumption, especially for reducing energy consumption of miscellaneous electric loads (MELs) due to direct control of occupants over MELs. Therefore, tracking MELs consumption and linking it with occupants’ energy-saving behavior is critical in intervening occupants’ energy-use behaviors. Currently, individual plug-load meters at an individual’s workspace are mainly used for tracking MELs in a commercial building. However, the implementation of this approach for full scale adoption requires a large initial investment on the part of the business. In addition, such an approach cannot assess occupants’ use on shared resources (e.g., lighting, shared office electronics). On the other hand, non-intrusive load-monitoring is considered a cost-effective and feasible tool to disaggregate building-level data for estimating appliance-specific energy consumption. Previous studies have suggested that adding occupancy sensing data to a load disaggregation process can help in economically estimating occupant-specific energy consumption. However, there is still a gap in properly linking appliance-specific energy consumption to occupants’ energy-use behaviors in commercial buildings. In response, this dissertation proposes an approach which tracks occupant-specific energy-use right after they enter to a building (entry event) and right before they leave a building (departure event); occupants’ behaviors at these events have a large impact on a building’s energy consumption. By utilizing density-based clustering and discriminant analysis, the approach couples occupancy information collected from Wi-Fi infrastructures with aggregated energy-load data to disaggregate load data down to the level of individual occupants. This critically helps understanding individual occupants’ energy-use behaviors in an economic manner and particularly allows to deliver tailored information through personalized feedback to an occupant who follows non-energy-saving behavior, to modify her energy actions to energy efficient behavior

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

    Get PDF
    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 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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