Inverse modeling to predict effective leakage area
MetadataShow full item record
The purpose of this research is to develop a new approach to estimate the effective leakage area using the inverse modeling process as an alternative to the blower door test. An actual office building, which is the head quarter of Energy Efficiency Hub, was used as an example case in this study. The main principle of the inverse modeling process is comparing the real monitor boiler gas consumption with the result calculated from the EnergyPlus model with a dynamic infiltration rate input to find the best estimation of the parameter of effective leakage area (ELA). This thesis considers only the feasibility of replacing the blower door test with the calibration approach, so rather than attempting an automated calibration process based on inverse modeling we deal with generating a first estimate and consider the role of model uncertainties that would make the proposed method less feasible. There are five steps of the whole process. First, we need to customize our own actual weather data (AMY) needed by the energy model (EnergyPlus model), which can help increase our quality of the result. Second, create the building energy model in EnergyPlus. Third, create a multi-zone model using CONTAM with different ELA estimation of each facade to calculate the dynamic infiltration rate of each ELA estimate. Fourth, input the dynamic infiltration rate got from the CONTAM model to EnergyPlus model and output the boiler energy consumption. Fifth, compare the boiler gas consumption from the model and the real monitor data and find the best match between the two and the corresponding ELA, which gives the best estimate from the whole inverse modeling process. From the simulation result comparison, the best estimation of the total building ELA from the inverse modeling process is the 23437cm2 at 4pa, while the result from the blower door test is 10483cm2 at 4pa. Because of the insufficient information of the building and also the uncertainty of the input parameters, the study has not led to a definite statement whether the proposed calibration of the ELA with consumption data can replace a blower door test to get an equally valid or even better ELA estimate, but it looks feasible. As this this case study is done in a deterministic context, the full feasibility test should be conducted under uncertainty. A first step towards this will talk be discussed in chapter 4.