Predictability of occupant presence and performance gap in building energy simulation

Ahn, Ki-Uhn; Kim, Deuk-Woo; Park, Cheol-Soo; Wilde, Pieter de

doi:10.1016/j.apenergy.2017.04.083

Cited by 80 publications

(33 citation statements)

References 35 publications

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“…Namely, Chang's model [27] to simulate the occupancy state of a space, Page's model [28] to simulate the number of occupants in a space, and Wang's model [29] to generate the spatial location of each occupant and the space-level occupancy for the whole building. As part of IEA EBC Annex 66 and Annex 79 data-based models and agent-based models for building occupancy have been developed [30,31,32,33,34,35,36]. In [31], data-mining methods were used to derive office occupancy schedules from appliance power consumption measurements.…”

Section: Advanced Occupant Behavior Modelsmentioning

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: Advanced Occupant Behavior Modelsmentioning

confidence: 99%

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

Happle

Fonseca

Schlueter

2020

Building and Environment

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“…Markov chain is a relatively more accurate and mature method in occupancy prediction [44]. The future state relies on the present state and the probability of the state change, which is represented by the transition probability matrix, is time independent as shown in Equation (1).…”

Section: Occupancy Schedulesmentioning

confidence: 99%

A novel stochastic modeling method to simulate cooling loads in residential districts

Yan

Hong

et al. 2017

Applied Energy

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District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized; this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2) occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution, and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

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“…The random walk is a mathematical formalization of a path that consists of a succession of random steps (Equation (2)) [20,21]. The process disturbance (q L , Equation (4)) is defined as follows: Interestingly, the aggregated process disturbance (Equation (4)) depends on the characteristics of occupant behavior [20][21][22].…”

Section: Random Walk Hypothesis For the Process Disturbancementioning

confidence: 99%

Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

Kim

Park

2017

Sustainability

Self Cite

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This paper addresses an application of the Kalman filter for estimating a time-varying process disturbance in a building space. The process disturbance means a synthetic composite of heat gains and losses caused by internal heat sources e.g., people, lights, equipment), and airflows. It is difficult to measure and quantify the internal heat sources and airflows due to their dynamic nature and time-lag impact on indoor environment. To address this issue, a Kalman filter estimation method was used in this study. The Kalman filtering is well suited for situations when state variables of interest cannot be measured. Based on virtual and real experiments conducted in this study, it was found that the Kalman filter can be used to estimate the time-varying process disturbance in a building space.

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Predictability of occupant presence and performance gap in building energy simulation

Cited by 80 publications

References 35 publications

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

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

A novel stochastic modeling method to simulate cooling loads in residential districts

Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

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