This thesis has improved Baltazar's methodology for potential energy savings estimation from retro-commissioning/retrofits measures. Important improvements and discussions are made on optimization parameters, limits on optimization parameter values, minimum airflow setting for VAV systems, space load calculation, simulation of buildings with more than one type of system, AHU shutdown simulation, and air-side simulation models. A prototype computer tool called the Potential Energy Savings Estimation (PESE) Toolkit is developed to implement the improved methodology and used for testing. The implemented methodology is tested in two retro-commissioned on-campus buildings with hourly measured consumption data. In the Sanders Corps of Cadets Center, the optimized profiles of parameter settings in single parameter optimizations can be explained with engineering principles. It reveals that the improved methodology is implemented correctly in the tool. The case study on the Coke Building shows that the improved methodology can be used in buildings with more than one system type. The methodology is then used to estimate annual potential energy cost savings for 14 office buildings in Austin, TX with very limited information and utility bills. The methodology has predicted an average total potential savings of 36% for SDVAV systems with electric terminal reheat, 22% for SDVAV systems with hot water reheat, and 25% for DDVAV systems. The estimations are compared with savings predicted in the Continuous Commissioning assessment report. The results show it may be helpful to study the correlation by using generalized factors of assessment predicted energy cost savings to estimated potential energy cost savings. The factors identified in this application are 0.68, 0.66, and 0.61 for each type of system. It is noted that one should be cautious in quoting these factors in future projects. In the future, it would be valuable to study the correlation between measured savings and estimated potential savings in a large number of buildings with retrocommissioning measures implemented. Additionally, further testing and modifications on the PESE Toolkit are necessary to make it a reliable software tool.

Energy recovery ventilation (ERV) systems are designed to decrease the energy consumed by building HVAC systems. ERV’s scavenge sensible and latent energy from the exhaust air leaving a building or space and recycle this energy content to pre-condition the entering outdoor air. A few studies found in the open literature are dedicated to developing detailed numerical models to predict or simulate the performance of energy recovery wheels and desiccant wheels. However, the models are often computationally intensive, requiring a lot of time to perform parametric studies. For example, if the physical characteristics of a study target change (e.g., wheel diameter or depth) or if the system runs at different operating conditions (e.g., wheel rotation speed or airflow rate), the model parameters need to be recalculated. Hence, developing a mapping method with better computational efficiency, which will enable the opportunity to conduct extensive parametric or optimal design studies for different wheels is the goal of this research. In this work, finite difference method (FDM) numerical models of energy recovery wheels and desiccant wheels are established and validated with laboratory test results. The FDM models are then used to provide data for the development of performance mapping methods for an energy wheel or a desiccant wheel. After validating these new mapping approaches, they are employed using independent data sets from different laboratories and other sources available in the literature to identify their universality. One significant characteristic of the proposed mapping methods that makes the contribution unique is that once the models are trained, they can be used to predict performance for other wheels with different physical geometries or different operating conditions if the desiccant material is identical. The methods provide a computationally efficient performance prediction tool; therefore, they are ideal to integrate with transient building energy simulation software to conduct performance evaluations or optimizations of energy recovery/ desiccant wheel integrated HVAC systems.