<?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%">Li, Han</style></author><author><style face="normal" font="default" size="100%">Hong, Tianzhen</style></author><author><style face="normal" font="default" size="100%">Sofos, Marina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title><short-title><style face="normal" font="default" size="100%">Energy and Buildings</style></short-title></titles><keywords><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%">Inverse problems</style></keyword><keyword><style  face="normal" font="default" size="100%">people count</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor data</style></keyword><keyword><style  face="normal" font="default" size="100%">zone air parameters</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-09-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">198</style></volume><pages><style face="normal" font="default" size="100%">228 - 242</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.&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>