Integrating physics-based models with sensor data: An inverse modeling approach
| Autor: | Sang Hoon Lee, Tianzhen Hong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Environmental Engineering
Environmental Science and Management Geography Planning and Development 0211 other engineering and technologies Inverse 02 engineering and technology 010501 environmental sciences 01 natural sciences law.invention Affordable and Clean Energy law Building performance simulation Architecture Building Thermal mass 021108 energy 0105 earth and related environmental sciences Civil and Structural Engineering Building & Construction Measure (data warehouse) Internal thermal mass Infiltration Building energy Inverse model Control engineering Building and Construction Physics based Thermostat Software deployment EnergyPlus Sensor data Energy (signal processing) |
| Zdroj: | Hong, Tianzhen; & Lee, Sang Hoon. (2019). Integrating physics-based models with sensor data: An inverse modeling approach. Building and Environment, 154, 23-31. doi: 10.1016/j.buildenv.2019.03.006. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/07w1t272 |
| ISSN: | 0360-1323 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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