Abstract
Bayesian probability theory offers a powerful framework for the calibration of building energy models (Bayesian calibration). The major issues impeding
its routine adoption are its steep learning curve, and the complicated setting up of the required calculation. This paper introduces CALIBRO, an R package
which has the objective of facilitating the undertaking of Bayesian calibration of building energy models. An overview of the techniques and procedures involved in CALIBRO is given, as well as demonstrations of its capability and reliability through two examples.
its routine adoption are its steep learning curve, and the complicated setting up of the required calculation. This paper introduces CALIBRO, an R package
which has the objective of facilitating the undertaking of Bayesian calibration of building energy models. An overview of the techniques and procedures involved in CALIBRO is given, as well as demonstrations of its capability and reliability through two examples.
| Original language | English |
|---|---|
| Publication status | Published - 7 Aug 2017 |
| Event | Building Simulation 2017: The 15th Biennial Conference of the International Building Performance Simulation Association (IBPSA) - Hyatt Regency Embarcadero, San Fransisco, United States Duration: 7 Aug 2017 → 9 Aug 2017 Conference number: 15 http://www.buildingsimulation2017.org/ |
Conference
| Conference | Building Simulation 2017 |
|---|---|
| Abbreviated title | BS17 |
| Country/Territory | United States |
| City | San Fransisco |
| Period | 7/08/17 → 9/08/17 |
| Internet address |
Keywords
- CALIBRO
- building energy models
- building energy simulation (BES)
- Bayesian probability theory
- Bayesian calibration