Integrating physics-based models with sensor data: An inverse modeling approach

TitleIntegrating physics-based models with sensor data: An inverse modeling approach
Publication TypeJournal Article
Year of Publication2019
AuthorsTianzhen Hong, Sang Hoon Lee
JournalBuilding and Environment
Volume154
Pagination23 - 31
Date Published05/2019
ISSN03601323
Keywordsbuilding performance simulation, energyplus, infiltration, internal thermal mass, inverse model, sensor data
Abstract

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.

URLhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930160X
DOI10.1016/j.buildenv.2019.03.006
Short TitleBuilding and Environment