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Title: Representation and evolution of urban weather boundary conditions in downtown Chicago

Abstract

Our study presents a novel computing technique for data exchange and coupling between a high-resolution weather simulation model and a building energy model, with a goal of evaluating the impact of urban weather boundary conditions on energy performance of urban buildings. The Weather Research and Forecasting (WRF) model is initialized with the operational High-Resolution Rapid Refresh (HRRR) dataset to provide hourly weather conditions over the Chicago region. We utilize the building footprint, land use, and building stock datasets to generate building energy models using EnergyPlus. We mapped the building exterior surfaces to local air nodes to import simulated microclimate data and to export buildings' heat emissions to their local environment. Preliminary experiments for a test area in Chicago show that predicted building cooling energy use differs by about 4.7% for the selected date when compared with simulations using TMY weather data and without considering the urban microclimate boundary conditions.

Authors:
 [1];  [2];  [1];  [2];  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Div.
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:
1484209
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Journal of Building Performance Simulation
Additional Journal Information:
Journal Volume: none; Journal Issue: none; Journal ID: ISSN 1940-1493
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Jain, Rajeev, Luo, Xuan, Sever, Gökhan, Hong, Tianzhen, and Catlett, Charlie. Representation and evolution of urban weather boundary conditions in downtown Chicago. United States: N. p., 2018. Web. doi:10.1080/19401493.2018.1534275.
Jain, Rajeev, Luo, Xuan, Sever, Gökhan, Hong, Tianzhen, & Catlett, Charlie. Representation and evolution of urban weather boundary conditions in downtown Chicago. United States. doi:10.1080/19401493.2018.1534275.
Jain, Rajeev, Luo, Xuan, Sever, Gökhan, Hong, Tianzhen, and Catlett, Charlie. Wed . "Representation and evolution of urban weather boundary conditions in downtown Chicago". United States. doi:10.1080/19401493.2018.1534275.
@article{osti_1484209,
title = {Representation and evolution of urban weather boundary conditions in downtown Chicago},
author = {Jain, Rajeev and Luo, Xuan and Sever, Gökhan and Hong, Tianzhen and Catlett, Charlie},
abstractNote = {Our study presents a novel computing technique for data exchange and coupling between a high-resolution weather simulation model and a building energy model, with a goal of evaluating the impact of urban weather boundary conditions on energy performance of urban buildings. The Weather Research and Forecasting (WRF) model is initialized with the operational High-Resolution Rapid Refresh (HRRR) dataset to provide hourly weather conditions over the Chicago region. We utilize the building footprint, land use, and building stock datasets to generate building energy models using EnergyPlus. We mapped the building exterior surfaces to local air nodes to import simulated microclimate data and to export buildings' heat emissions to their local environment. Preliminary experiments for a test area in Chicago show that predicted building cooling energy use differs by about 4.7% for the selected date when compared with simulations using TMY weather data and without considering the urban microclimate boundary conditions.},
doi = {10.1080/19401493.2018.1534275},
journal = {Journal of Building Performance Simulation},
issn = {1940-1493},
number = none,
volume = none,
place = {United States},
year = {2018},
month = {11}
}