The goal of this dissertation is to demonstrate that data from a remotely located building can be utilized for energy modeling of a similar type of building and to demonstrate how to use this remote data without physically moving the data from one server to another using Virtual Information Fabric Infrastructure (VIFI). In order to achieve this goal, firstly an EnergyPlus model was created for Greek Life Center, a campus building located at University of North Texas campus at Denton in Texas, USA. Three thermal comfort models of Fanger model, Pierce two-node model and KSU two-node model were compared in order to find which one of these three models is most accurate to predict occupant thermal comfort. This study shows that Fanger's model is most accurate in predicting thermal comfort. Secondly, an experimental data pertaining to lighting usage and occupancy in a single-occupancy office from Carnegie Mellon University (CMU) has been implemented in order to perform energy analysis of Greek Life Center assuming that occupants in this building's offices behave similarly as occupants in CMU. Thirdly, different data types, data formats and data sources were identified which are required in order to develop a city-scale urban building energy model (CS-UBEM). …

The study focuses on the sensitivity and uncertainty analysis of occupancy-related parameters using Energyplus modeling method. The model is based on a real building Zero Energy Lab in Discovery Park, at University of North Texas. Four categories of parameters are analyzed: heating/cooling setpoint, lighting, equipment and occupancy. Influence coefficient (IC) is applied in the sensitivity study, in order to compare the impact of individual parameter on the overall building energy consumption. The study is conducted under Texas weather file as well as North Dakota weather file in order to find weather’s influence of sensitivity. Probabilistic collocation method (PCM) is utilized for uncertainty analysis, with an aim of predicting future energy consumption based on history or reference data set. From the study, it is found that cooling setpoint has the largest influence on overall energy consumption in both Texas and North Dakota, and occupancy number has the least influence. The analysis also indicates schedule’s influence on energy consumption. PCM is able to accurately predict future energy consumption with limited calculation, and has great advantage over Monte Carlo Method. The polynomial equations are generated in both 3-order and 6-order, and the 6-order equation is proved to have a better result, which is …

This research investigates the bio-products/phase change material (PCM) composites for the building envelope application. Bio-products, such as wood and herb, are porous medium, which can be applied in the building envelope for thermal insulation purpose. PCM is infiltrated into the bio-product (porous medium) to form a composite material. The PCM can absorb/release large amount of latent heat of fusion from/to the building environment during the melting/solidification process. Hence, the PCM-based composite material in the building envelope can efficiently adjust the building interior temperature by utilizing the phase change process, which improves the thermal insulation, and therefore, reduces the load on the HVAC system. Paraffin wax was considered as the PCM in the current studies. The building energy savings were investigated by comparing the composite building envelope material with the conventional material in a unique Zero-Energy (ZØE) Research Lab building at University of North Texas (UNT) through building energy simulation programs (i.e., eQUEST and EnergyPlus). The exact climatic conditions of the local area (Denton, Texas) were used as the input values in the simulations. It was found that the EnergyPlus building simulation program was more suitable for the PCM based building envelope using the latent heat property. Therefore, based on the …