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Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels

Pang, Zhihong, O'Neill, Zheng
Applied energy 2018 v.232 pp. 424-442
Monte Carlo method, buildings, carbon, case studies, climate, electric energy consumption, emissions, energy, equipment, heating systems, models, prototypes, sampling, uncertainty, water utilization, California, Florida, Illinois, Texas, Vermont
The water heating system is a major contributor to building energy consumption and carbon emissions in the United States, especially for the Hotel/Motel sector. Various factors in the design and operation stages are found to have great influences on the hot water usage and associated energy usage. There has been an increased number of studies on optimizing the design and sizing of the water heating system in commercial buildings in recent years. However, most of these studies focused on the collection and analysis of the actual data of hot water usage with rare acknowledgments of uncertainties from a variety of influential parameters such as occupant behaviors and operational schedules. The current understanding of the sensitivity of the hot water usage related to these influential factors is still limited.This paper aims to conduct an uncertainty and sensitivity analysis (UA & SA) to investigate the behavior of the domestic hot water (DHW) usage in hotels and its key influencing factors. An EnergyPlus Monte Carlo simulation is performed by using the large hotel building prototype model developed by the U.S. DOE as the baseline model. 161 input parameters ranging from equipment parameters (e.g., size, efficiency, operation schedules, etc.) to occupant behaviors are perturbed using Monte Carlo and Karhunen-Loève expansion sampling methods. Eight outputs associated with the hot water usage (i.e., the peak/annual whole building water consumptions, DHW system water consumption, DHW system gas consumptions, and DHW system electricity consumptions) are specified as the outputs of interest. Five locations, which are Burlington, VT; Chicago, IL; San Francisco, CA; Houston, TX; and Miami, FL are selected to investigate the influence of the climate condition. 3000 sample EnergyPlus files are created for each location. Two indicators (i.e., the PEAR index, and the variance-based Sobol index) are computed for the sensitivity analysis. It suggests that the SA results from the PEAR index and the Sobol index are very similar in this case study.