@article {30312,
	title = {Spatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings},
	journal = {Energy and Buildings},
	year = {2017},
	abstract = {<p>Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.</p>},
	keywords = {air handling unit, chiller plant, equipment sizing, internal heat gain, spatial distribution, spatial diversity, stochastic},
	doi = {10.1016/j.enbuild.2017.01.044},
	author = {Qi Zhang and Da Yan and Jingjing An and Tianzhen Hong and Wei Tian and Kaiyu Sun}
}
@article {59961,
	title = {Simulation of Occupancy in Buildings},
	journal = {Energy and Buildings},
	volume = {87},
	year = {2015},
	month = {01/2015},
	pages = {348-359},
	abstract = {<p>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{\textendash}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.</p>},
	keywords = {building simulation, co-simulation, occupancy, occupant behavior, software module, stochastic modeling},
	doi = {10.1016/j.enbuild.2014.11.067},
	author = {Xiaohang Feng and Da Yan and Tianzhen Hong}
}
