01992nas a2200229   4500008004100000022001300041245008400054210006900138260001200207300001200219490000800231520128900239653003601528653001501564653001701579653002601596653001801622653001601640100001901656700002001675856006701695       2019                        eng d  a0360132300aIntegrating physics-based models with sensor data: An inverse modeling approach0 aIntegrating physicsbased models with sensor data An inverse mode  c05/2019  a23 - 310 v1543 a<p>Physics-based building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing buildings.</p>10abuilding performance simulation10aenergyplus10ainfiltration10ainternal thermal mass10ainverse model10asensor data1 aHong, Tianzhen1 aLee, Sang, Hoon  uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930160X