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Title: A novel stochastic modeling method to simulate cooling loads in residential districts

Abstract

District cooling systems are widely used in urban residential communities in China. Most of such systems are oversized, which leads to wasted investment, low operational efficiency and, thus, waste of energy. The accurate prediction of district cooling loads that can support the rightsizing of cooling plant equipment remains a challenge. This study develops a novel stochastic modeling method that consists of (1) six prototype house models representing most apartments in a district, (2) occupant behavior models of residential buildings reflecting their spatial and temporal diversity as well as their complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was applied to a case study using the Designer's Simulation Toolkit (DeST) to simulate the cooling loads of a residential district in Wuhan, China. The simulation results agreed well with the measured data based on five performance metrics representing the aggregated cooling consumption, the peak cooling loads, the spatial load distribution, the temporal load distribution and the load profiles. Two prevalent simulation methods were also employed to simulate the district cooling loads. Here, the results showed that oversimplified assumptions about occupant behavior could leadmore » to significant overestimation of the peak cooling load and the total cooling loads in the district. Future work will aim to simplify the workflow and data requirements of the stochastic method for its application, and to explore its use in predicting district heating loads and in commercial or mixed-use districts.« less

Authors:
 [1];  [1];  [2];  [2]
  1. Tsinghua Univ., Beijing (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1436650
Alternate Identifier(s):
OSTI ID: 1550004
Grant/Contract Number:  
[AC02-05CH11231]
Resource Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
[ Journal Volume: 206; Journal Issue: C]; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Stochastic modeling; Occupant behavior; Residential district; DeST; Cooling load; Building performance simulation

Citation Formats

An, Jingjing, Yan, Da, Hong, Tianzhen, and Sun, Kaiyu. A novel stochastic modeling method to simulate cooling loads in residential districts. United States: N. p., 2017. Web. doi:10.1016/j.apenergy.2017.08.038.
An, Jingjing, Yan, Da, Hong, Tianzhen, & Sun, Kaiyu. A novel stochastic modeling method to simulate cooling loads in residential districts. United States. doi:10.1016/j.apenergy.2017.08.038.
An, Jingjing, Yan, Da, Hong, Tianzhen, and Sun, Kaiyu. Mon . "A novel stochastic modeling method to simulate cooling loads in residential districts". United States. doi:10.1016/j.apenergy.2017.08.038. https://www.osti.gov/servlets/purl/1436650.
@article{osti_1436650,
title = {A novel stochastic modeling method to simulate cooling loads in residential districts},
author = {An, Jingjing and Yan, Da and Hong, Tianzhen and Sun, Kaiyu},
abstractNote = {District cooling systems are widely used in urban residential communities in China. Most of such systems are oversized, which leads to wasted investment, low operational efficiency and, thus, waste of energy. The accurate prediction of district cooling loads that can support the rightsizing of cooling plant equipment remains a challenge. This study develops a novel stochastic modeling method that consists of (1) six prototype house models representing most apartments in a district, (2) occupant behavior models of residential buildings reflecting their spatial and temporal diversity as well as their complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was applied to a case study using the Designer's Simulation Toolkit (DeST) to simulate the cooling loads of a residential district in Wuhan, China. The simulation results agreed well with the measured data based on five performance metrics representing the aggregated cooling consumption, the peak cooling loads, the spatial load distribution, the temporal load distribution and the load profiles. Two prevalent simulation methods were also employed to simulate the district cooling loads. Here, the results showed that oversimplified assumptions about occupant behavior could lead to significant overestimation of the peak cooling load and the total cooling loads in the district. Future work will aim to simplify the workflow and data requirements of the stochastic method for its application, and to explore its use in predicting district heating loads and in commercial or mixed-use districts.},
doi = {10.1016/j.apenergy.2017.08.038},
journal = {Applied Energy},
number = [C],
volume = [206],
place = {United States},
year = {2017},
month = {9}
}

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