02132nas a2200241   4500008004100000022001300041245011800054210006900172260001600241300001400257490000800271520136600279653001501645653001701660653002101677653001701698653001601715653002401731100001201755700001901767700001801786856008601804       2019                        eng d  a0378778800aAn inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data0 ainverse approach to solving zone air infiltration rate and peopl  cJan-09-2019  a228 - 2420 v1983 a<p>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.</p>10aenergyplus10ainfiltration10aInverse problems10apeople count10asensor data10azone air parameters1 aLi, Han1 aHong, Tianzhen1 aSofos, Marina  uhttps://simulationresearch.lbl.gov/publications/inverse-approach-solving-zone-air