TY - JOUR
T1 - Data Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China
JF - Energy and Buildings
Y1 - 2015/01//
SP - 275
EP - 287
A1 - Xin Zhou
A1 - Da Yan
A1 - Tianzhen Hong
A1 - Xiaoxin Ren
KW - building simulation
KW - energy use
KW - Lighting modeling
KW - occupant behavior
KW - office buildings
KW - Poisson distribution
KW - stochastic modeling
AB - Lighting consumes about 20% to 40% of the total electricity use in large office buildings in China. Commonly in building simulations, static time schedules for typical weekdays, weekends and holidays are assumed to represent the dynamics of lighting energy use in buildings. This approach does not address the stochastic nature of lighting energy use, which can be influenced by occupant behavior in buildings. This study analyzes the main characteristics of lighting energy use over various timescales, based on the statistical analysis of measured lighting energy use data from 15 large office buildings in Beijing and Hong Kong. It was found that in these large office buildings, the 24-hourly variation in lighting energy use was mainly driven by the schedules of the building occupants. Outdoor illuminance levels had little impact on lighting energy use due to the lack of automatic daylighting controls (an effective retrofit measure to reduce lighting energy use) and the relatively small perimeter area exposed to natural daylight. A stochastic lighting energy use model for large office buildings was further developed to represent diverse occupant activities, at six different time periods throughout a day, and also the annual distribution of lighting power across these periods. The model was verified using measured lighting energy use from the 15 buildings. The developed stochastic lighting model can generate more accurate lighting schedules for use in building energy simulations, improving the simulation accuracy of lighting energy use in real buildings.
VL - 86
U2 - LBNL-180389
DO - 10.1016/j.enbuild.2014.09.071
ER -
TY - JOUR
T1 - Simulation of Occupancy in Buildings
JF - Energy and Buildings
Y1 - 2015/01//
SP - 348
EP - 359
A1 - Xiaohang Feng
A1 - Da Yan
A1 - Tianzhen Hong
KW - building simulation
KW - co-simulation
KW - occupancy
KW - occupant behavior
KW - software module
KW - stochastic modeling
AB - Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.
VL - 87
U2 - LBNL-180424
DO - 10.1016/j.enbuild.2014.11.067
ER -
TY - JOUR
T1 - Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration
Y1 - 2014/
A1 - Kaiyu Sun
A1 - Tianzhen Hong
A1 - Siyue Guo
KW - building energy use
KW - building simulation
KW - model calibration
KW - occupant behavior
KW - overtime occupancy
KW - stochastic modeling
AB - Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.
U2 - LBNL-6670E
ER -