International survey on current occupant modelling approaches in building performance simulation

O’Brien, William; Gaetani, II Isabella; Gilani, Sara; Carlucci, Salvatore; Hoes, P Pieter-Jan; Hensen, Jlm Jan

doi:10.1080/19401493.2016.1243731

Cited by 58 publications

(28 citation statements)

References 36 publications

(41 reference statements)

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“…1a. Publisher Purpose DOE (1979) [9] Development of Building Energy Performance Standards (BEPS) legislation ASHRAE (1989) [10] Building Energy Cost Budget (ECB) Method: Code compliance via simulation ASHRAE (2004) [11] Building Performance Rating Method: Estimation of % energy savings of advanced building designs DOE/NREL (2011) [12] DOE commercial building research: technology assessment, design optimization, analyze advanced controls, develop energy codes and standards, and to conduct lighting, daylighting, ventilation, and indoor air quality studies ASHRAE (2013) [13,14] Envelope trade-off: Envelope code compliance via simulation On the building-scale, standard schedules are widely used for code compliance, systems design, and research purposes as intended by their publishers [12,21,22].…”

Section: Origins and Purposes Of Standard Occupancy Schedulesmentioning

confidence: 99%

Context-specific urban occupancy modeling using location-based services data

Happle

Fonseca

Schlueter

2020

Building and Environment

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Energy-related occupant behavior is a major source of uncertainty in building and urban energy performance simulations. Standardized assumptions, published by ASHRAE and others in the form of occupancy schedules, are widely used in research and practice, especially on the district-scale. In this work, we gathered location-based services data to create context-specific, data-driven occupancy schedules. Using a web mapping service, we collected data for retail and restaurant uses in the downtown neighborhoods of 13 different U.S. cities to create data-driven schedules for each context. The schedules were compared to ASHRAE standard assumptions using the earth mover's distance approach and the schedules' energy-related features. We found that standard schedules seem to significantly overestimate weekly building occupancy, although the shapes of the schedules are generally similar. The use of standard schedules could therefore, have significant impacts on district-scale energy demand simulations, as the overestimation will be cumulative.As compared to the differences between data-driven and standard schedules, the differences between different locations are significantly smaller. However in extreme cases, the weekly cumulative occupancy and the number of occupied hours differ by more than 30% between locations, which means that context-specific differences together with climatic differences might also impact building performance simulation results. Furthermore, we found differences in daily data between the different days of the week. In particular, the observed behavior on Fridays is significantly different from other weekdays for both considered use-types. This indicates that the conventional categorization of occupant behavior models into three day-types: weekday, Saturday, and Sunday, should be reconsidered.

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Section: Origins and Purposes Of Standard Occupancy Schedulesmentioning

confidence: 99%

Context-specific urban occupancy modeling using location-based services data

Happle

Fonseca

Schlueter

2020

Building and Environment

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“…Based on this literature review, a number of shortcomings of the existing models may be revealed. First, models in literature do not account sufficiently to household variables while quantifying residential energy consumption [49]. In most of the approaches, the only variable considered for representing households' attributes is the number of occupants (e.g.…”

Section: Research Gaps With Regard To Occupant-related Energy Consumpmentioning

confidence: 99%

“…Moreover, the framework allows assessing energy consumption variability between different household profiles. Given these features, such a model may thus be coupled to industrial energy simulators to produce more accurate energy estimations with a reduced uncertainty [49]. This is a substantial point for addressing the growing market of 'energy performance contracts'.…”

mentioning

confidence: 99%

An activity-based modelling framework for quantifying occupants’ energy consumption in residential buildings

Leroy

Yannou

2018

Computers in Industry

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The residential building is a major energy consumer and pollution source worldwide. The shift towards constructing energy-efficient buildings is impelling higher performance. In sustainable building, occupants become a major source of uncertainty in energy consumption. Yet, energy simulation tools often account for occupant behaviour through predefined fixed consumption profiles. Therefore, energy and buildings experts are in need for more precise methods for better forecasting the influence of occupants on the building performance. An activity-based framework for quantifying occupant-related energy consumption is proposed. The energy consumption is quantified per domestic activity as a function of households' socio-demographic and economic attributes. The aggregation of such domestic activity energy consumption provides an accurate estimation of the household energy consumption per daily, monthly and annually periods. First, a literature review about residential energy consumption and the existing modelling approaches is presented. Second, a systematic breakdown structure of energy end-uses is proposed. The activity-based framework is then introduced. An application example is demonstrated together with simulation results. Finally, model's utility is outlined and its possible implications are discussed.The residential sector consumes secondary energy, i.e. electricity and hydrogen produced from primary energy sources such as coal, natural gas, petroleum, nuclear energy and renewable energy sources, which is used by occupants for performing their domestic activities. Several studies pointed out the major end-use groups of secondary energy such as space heating, space cooling, domestic hot water, as well as appliances and lighting [14][15][16][17]. Building's energy consumption is highly dependent on the performance of its systems and the general behaviour of the occupants [18]. According to Robinson [19], the most complex processes taking place within buildings are those that result from human behaviour. Authors such as Emery and Kippenhan [20], Masoso and Grobler (2010), and Guerra-Santin and Itard [21] also reveal occupants' influence on residential energy consumption. Authors such as Page et al. [22], Robinson [19] and Wilke et al. [18] show that the influence of occupants can be modelled by their presence, the actions they perform (activities such as cooking, using light, etc.), as well as their interactions with the controls of inherent building systems designed for adjusting the indoor environment (e.g., lighting and HVAC).

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“…A survey on occupant modeling approaches in building simulations [5] showed that 66% of the researchers modeled the thermostat set point as a daily schedule, while another 16% used a constant for the entire year. Very few researchers have explicitly acknowledged the occupants' interaction with the thermostats.…”

Section: Introductionmentioning

confidence: 99%

Impact of occupant behavior on energy use of HVAC system in offices

Deng

Chen

2019

E3S Web Conf.

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The current methods for simulating building energy consumption are often inaccurate, and the error could be as large as 150%. Various types of occupant behavior may explain this inaccuracy. Therefore, it is important to identify an approach to estimate the impact of the behaviors on the energy consumption. The present study used EnergyPlus program to simulate the energy consumption of the HVAC system in an office building by implementing a behavioral artificial neural network (ANN) model. The behavioral ANN model calculates the probability of behavior occurrence according to indoor air temperature, relative humidity, clothing level and metabolic rate. The probability was used to predict energy use in 20 offices for one month in winter, spring and summer in 2018, respectively. Measured energy data from the offices were used to validate the simulated results. When a behavioral artificial neural network (ANN) model was implemented in the energy simulation, the difference between the simulated results and the measured data was less than 13%. Energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Our further simulations found that adjustment of thermostat set point and clothing level by occupants could lead to 25% and 15% energy use variation in interior offices and exterior offices, respectively.

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International survey on current occupant modelling approaches in building performance simulation

Cited by 58 publications

References 36 publications

Context-specific urban occupancy modeling using location-based services data

Context-specific urban occupancy modeling using location-based services data

An activity-based modelling framework for quantifying occupants’ energy consumption in residential buildings

Impact of occupant behavior on energy use of HVAC system in offices

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