<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Chen, Yixing</style></author><author><style face="normal" font="default" size="100%">Luo, Xuan</style></author><author><style face="normal" font="default" size="100%">Luo, Na</style></author><author><style face="normal" font="default" size="100%">Lee, Sang Hoon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ten questions on urban building energy modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-01-2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">168</style></volume><pages><style face="normal" font="default" size="100%">106508</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings.&lt;br /&gt;Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers.&lt;br /&gt;This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lee, Sang Hoon</style></author><author><style face="normal" font="default" size="100%">Hong, Tianzhen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Validation of an inverse model of zone air heat balance</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Energy simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">infiltration</style></keyword><keyword><style  face="normal" font="default" size="100%">internal thermal mass</style></keyword><keyword><style  face="normal" font="default" size="100%">inverse model</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-08-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">161</style></volume><pages><style face="normal" font="default" size="100%">106232</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL’s Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be used to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.&lt;/p&gt;</style></abstract></record></records></xml>