@article {31669,
	title = {A novel approach for selecting typical hot-year (THY) weather data},
	journal = {Applied Energy},
	volume = {242},
	year = {2019},
	month = {03/2019},
	pages = {1634 - 1648},
	abstract = {<p>The global climate change has resulted in not only warmer climate conditions but also more frequent extreme weather events, such as heat waves. However, the impact of heat waves on the indoor environment has been investigated in a limited manner. In this research, the indoor thermal environment is analyzed using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events. The simulation results were used to analyze the indoor environment during hot summers. A new kind of weather data referred to as the typical hot year was defined and selected based on the simulated indoor environment during heat waves. The typical hot-year weather data can be used to simulate the indoor environment during extreme heat events and for the evaluation of effective technologies and strategies to mitigate against the impact of heat waves on the energy demand of buildings and human health. The limitations of the current study and future work are also discussed.</p>},
	keywords = {Actual weather data, dest, Heat wave, Multiyear simulation, Residential indoor thermal environment, Typical hot year},
	issn = {03062619},
	doi = {10.1016/j.apenergy.2019.03.065},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261919304659},
	author = {Siyue Guo and Da Yan and Tianzhen Hong and Chan Xiao and Ying Cui}
}
@article {59969,
	title = {Integrated Design for High Performance Buildings},
	year = {2014},
	author = {Tianzhen Hong and Cheng Li and Richard C. Diamond and Da Yan and Qi Zhang and Xin Zhou and Siyue Guo and Kaiyu Sun and Jingyi Wang}
}
@article {59964,
	title = {Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration},
	year = {2014},
	abstract = {<p>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.</p>},
	keywords = {building energy use, building simulation, model calibration, occupant behavior, overtime occupancy, stochastic modeling},
	author = {Kaiyu Sun and Tianzhen Hong and Siyue Guo}
}
