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

被引:55
作者
Pang, Zhihong [1 ]
O'Neill, Zheng [1 ]
机构
[1] Univ Alabama, Dept Mech Engn, Tuscaloosa, AL 35478 USA
关键词
Domestic hot water; Sensitivity analysis; Uncertainty analysis; Karhunen-Loeve expansion; ENERGY PERFORMANCE; DESIGN; MODEL; SIMULATION; METHODOLOGY; SYSTEM;
D O I
10.1016/j.apenergy.2018.09.221
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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-Loeve 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.
引用
收藏
页码:424 / 442
页数:19
相关论文
共 50 条
[31]   UNCERTAINTY QUANTIFICATION AND REDUCTION USING SENSITIVITY ANALYSIS AND HESSIAN DERIVATIVES [J].
Sanchez, Josefina ;
Otto, Kevin .
PROCEEDINGS OF ASME 2021 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2021, VOL 3B, 2021,
[32]   Sensitivity Analysis and Parametric Uncertainty Quantification of a Modular Multilevel Converter [J].
Rashidi, Niloofar ;
Burgos, Rolando ;
Roy, Chris ;
Boroyevich, Dushan .
JOURNAL OF VERIFICATION, VALIDATION AND UNCERTAINTY QUANTIFICATION, 2022, 7 (03)
[33]   Global sensitivity analysis for uncertainty quantification in fire spread models [J].
Ujjwal, K. C. ;
Aryal, Jagannath ;
Garg, Saurabh ;
Hilton, James .
ENVIRONMENTAL MODELLING & SOFTWARE, 2021, 143
[34]   Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis [J].
Abdel-Khalik, Hany S. ;
Bang, Youngsuk ;
Wang, Congjian .
ANNALS OF NUCLEAR ENERGY, 2013, 52 :28-46
[35]   Sensitivity analysis of the new sizing tool "PISTACHE" for solar heating, cooling and domestic hot water systems [J].
Semmari, Hamza ;
Marc, Olivier ;
Praene, Jean-Philippe ;
Le Denn, Amandine ;
Boudehenn, Francois ;
Lucas, Franck .
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SOLAR HEATING AND COOLING FOR BUILDINGS AND INDUSTRY (SHC 2013), 2014, 48 :997-1006
[36]   ERROR AND UNCERTAINTY QUANTIFICATION AND SENSITIVITY ANALYSIS IN MECHANICS COMPUTATIONAL MODELS [J].
Liang, Bin ;
Mahadevan, Sankaran .
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2011, 1 (02) :147-161
[37]   Dual mixed refrigerant LNG process: Uncertainty quantification and dimensional reduction sensitivity analysis [J].
Qyyum, Muhammad Abdul ;
Pham Luu Trung Duong ;
Le Quang Minh ;
Lee, Sanggyu ;
Lee, Moonyong .
APPLIED ENERGY, 2019, 250 :1446-1456
[38]   UNCERTAINTY QUANTIFICATION AND SENSITIVITY ANALYSIS OF MATERIAL PARAMETERS IN CRYSTAL PLASTICITY FINITE ELEMENT MODELS [J].
Khadyko, Mikhail ;
Sturdy, Jacob ;
Dumoulin, Stephane ;
Hellevik, Leif Rune ;
Hopperstad, Odd Sture .
JOURNAL OF MECHANICS OF MATERIALS AND STRUCTURES, 2018, 13 (03) :379-400
[39]   MODEL BASED ROOT CAUSE ANALYSIS OF MANUFACTURING QUALITY PROBLEMS USING UNCERTAINTY QUANTIFICATION AND SENSITIVITY ANALYSIS [J].
Otto, Kevin ;
Mosqueda, Josefina Sanchez .
PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2019, VOL 1, 2020,
[40]   Uncertainty and Sensitivity Analysis for Hot-Leg LOCA in Two-Loop PWR [J].
Prosek, Andrej .
NUCLEAR TECHNOLOGY, 2025,