The Grid Convergence Index ( $\mathrm{GCI}$) method requires at least three systematic mesh refinements, each requiring the numerical solution to be in the asymptotic range. When the observed order of accuracy differs from the formal order of accuracy, a lack of numerical precision exists which may result in erroneous conclusions. The aim of the work in this paper is to evaluate the effect the mesh resolution for structured hexahedral, unstructured hexahedral and tetrahedral mesh has on the observed order of accuracy and the accuracy of the numerical solution of the mean air flow velocity profile in indoor environments. The $\mathrm{GCI}$ value was calculated based on the recommendation of the lower limit of the observed order of accuracy. For the structured hexahedral mesh, with successive grid refinements the observed order of accuracy converges close to the formal order of accuracy, while for the two other unstructured meshes it converges more gradually. The lowest $\mathrm{GCI}$ value was obtained when the observed order of accuracy has reached its highest level of accuracy. In this study the $\mathit{RNG} k-\epsilon$ turbulence model was adopted, and when the obtained numerical results were compared with another published numerical study, the comparison showed that the structured hexahedral mesh with $\mathit{RNG} k-\epsilon$ turbulence model produced a similar result to that produced from unstructured hexahedral mesh using the $\mathit{LES}$ turbulence model.

In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.

Developing representative archetypes using a bottom up approach for stock modelling is an excellent tool for evaluating the overall performance of the building stock; however it requires detailed analysis of the various building types. Currently there is no detailed housing data base for Local Authority housing in Cork City, Ireland that catalogues the housing stocks geometric properties and thermal characteristics for each typology. This study details a methodology to catalogue the LAH stock and build a detailed housing data base. The GIS web based mapping application Google Street View is used to identify 20 house typologies across 36 LAH developments; a total of 10,449 housing units are counted and information subdivided into end of terrace, mid terrace, semi-detached, orientation and elevation. The data base provides the base line assessment for building a stock aggregation model; the stock aggregation approach is used as a method to evaluate the energy performance of the building stock, beginning with analysis of individual house types; referred to as a ‘bottom up approach’.4 representative archetypes are produced and modelled using DEAP simulation software. A 6% variation is recorded when compared to an averaged 121 registered BER results from LAH in Cork City. A full estimation of total energy use and CO2 is recorded for 10,449 units resulting in 211 GWh/y and 35,477 GtCO2/y. Investment in retro-fit is highly justified in this area with large potential for reducing CO2 emissions, the number of fuel poverty sufferers and victims of seasonal mortality due to thermally inefficient homes. The study suggests the method applied has scalable potential and is modular in structure facilitating wider adaption