Dynamic occupant density models of commercial buildings for urban energy simulation

Abstract

The number of occupants and its changing pattern over time are key information for building and urban energy simulation. However, the commonly used assumption and simplification of a fixed occupancy schedule does not reflect the complicated reality, leading to significant errors in energy simulation. Therefore, dynamic occupant density models which describe the real-world situation more accurately should be developed. This paper presents a methodology to develop such a model for commercial buildings and expand it from the building level to urban level. First, a total of 2275 commercial buildings in Nanjing, a major city in China, are identified and classified into three sub-categories using Points of Interest and logistic regression. Then field measurement is conducted to obtain the hourly occupant density for 12 sample commercial buildings. The building-level dynamic occupant density model is developed by fitting normal distribution functions into the measured data. Finally, transportation accessibility and population level, two urban parameters, are defined and used to expand the building-level occupant density model to the urban-level one. The dynamic urban-level occupant density model is verified for all three sub-categories of commercial buildings and the overall results are acceptable.