Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration.

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties.

Development of a Bayesian inference model for assessing ventilation condition based on CO meters in primary schools.

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO meters to indicate air quality, but how to interpret the data remains unclear.