Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining

Zhao, Jie; Lasternas, Bertrand; Lam, Khee Poh; Yun, Ray Jaeyung; Loftness, Vivian

doi:10.1016/j.enbuild.2014.07.033

Cited by 262 publications

(106 citation statements)

References 34 publications

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“…Studies indicate that low variability in energy intensity demonstrate that an occupant has strong energy habits. Therefore, interventions seeking to influence such rigid occupants are much harder to accomplish than interventions targeting occupants with flexible habits [12,99,101,122,123]. Furthermore, in addition to rigid occupants, extremists can affect the performance of occupancy-intervention tools.…”

Section: Improving Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

“…However, similar to Dounis and Caraiscos [93], they did not clearly respond to the balance between thermal comfort and energy conversation, which is important since achieving a level of thermal comfort might lead to increasing total energy consumption of a building. Zhao et al [99] developed a practical data-mining approach that collects the energy consumption data of various systems and appliances within office spaces to find occupants' passive energy behaviors. The proposed data-mining approach is based on nominal classification (i.e., C4.5 decision tree, locally weighted naïve bayes, and support vector machine) and numeric regression algorithms (i.e., linear regression and support vector regression).…”

Section: Other Techniquesmentioning

confidence: 99%

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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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Section: Improving Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

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

2015

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“…Studies which model the occupants' interaction with buildings and control systems have included lighting controls (Reinhart et al 2006), shading devices (Haldi and Robinson 2010) and ventilation (Yun et al 2008). Other studies have focused on occupancy presence, fundamental for occupancy research as most occupant behaviour patterns are influenced by occupancy (Roetzel et al 2010;D'Oca and Hong 2014;Zhao et al 2014;Feng et al 2015). Characterising the stochastic nature *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Occupant behaviour modelling in domestic buildings: the case of household electrical appliances

Yılmaz

Firth

Allinson

2017

Journal of Building Performance Simulation

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“…Much research is underway to identify the uncertainty of human behavior in buildings. Most of the research has been based on surveys, but in recent years, there has been an attempt to develop stochastic occupancy profiles for individual building using occupancy sensor (Duarte et al 2013;Wang et al 2016;Diraco et al 2015), Bluetooth positioning (Zhao et al 2014), and random process (Chen et al 2015;O'Neill and Niu 2017). The identification of actual occupancy schedule may contribute to accurate building energy forecasting and occupancy-based control.…”

Section: Uncertainty In Human Behaviormentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining

Cited by 262 publications

References 34 publications

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

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

Occupant behaviour modelling in domestic buildings: the case of household electrical appliances

Review on stochastic modeling methods for building stock energy prediction

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