The sensitivity of the important inputs and the savings prediction function reliability for the WinAM 4.3 software is studied in this research. WinAM was developed by the Continuous Commissioning (CC) group in the Energy Systems Laboratory at Texas A&M University. For the sensitivity analysis task, fourteen inputs are studied by adjusting one input at a time within ± 30% compared with its baseline. The Single Duct Variable Air Volume (SDVAV) system with and without the economizer has been applied to the square zone model. Mean Bias Error (MBE) and Influence Coefficient (IC) have been selected as the statistical methods to analyze the outputs that are obtained from WinAM 4.3. For the saving prediction reliability analysis task, eleven Continuous Commissioning projects have been selected. After reviewing each project, seven of the eleven have been chosen. The measured energy consumption data for the seven projects is compared with the simulated energy consumption data that has been obtained from WinAM 4.3. Normalization Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Squared Error (CV (RMSE)) statistical methods have been used to analyze the results from real measured data and simulated data. Highly sensitive parameters for each energy resource of the system with the economizer and the system without the economizer have been generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model’s quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated model.

This study evaluated how well energy savings persisted over time in ten university buildings that had undergone retro-commissioning in 1996. The savings achieved immediately following retro-commissioning and in three subsequent years were documented in a previous study (Cho 2002). The current study expanded on this previous study by evaluating the performance of each building over nine additional years. Follow up retro-commissioning work performed in each building during that time was documented, as well as changes to the energy management control system. Savings were determined in accordance with the methodology outlined in the International Performance Measurement and Verification Protocol (IPMVP 2007), with ASHRAE Guideline 14 also serving as a reference. Total annualized savings for all buildings in 1997 (the year just after retro-commissioning) were 45(plus or minus 2)% for chilled water, 67(plus or minue 2)% for hot water, and 12% for electricity. Combining consumption from the most recent year for each building with valid energy consumption data showed a total savings of 39(plus or minus 1)% for chilled water, 64(plus or minus 2)% for heating water, and 22% for electricity. Uncertainty values were calculated in accordance with methodology in the IPMVP and ASHRAE Guideline 14, and were reported at the 90% confidence interval. The most recent year of data for most of the buildings was 2008-2009, although a few of the buildings did not have valid consumption data for that year. Follow up work performed in the buildings, lighting retrofits, and building metering changes beginning in 2005 were the major issues believed to have contributed to the high level of savings persistence in later years. When persistence trends were evaluated with adjustment for these factors, average savings for the buildings studied were found to degrade over time, and exponential models were developed to describe this degradation. The study concluded that on average energy savings after retro-commissioning will degrade over time in a way that can be modeled exponentially. It was also concluded that high levels of savings persistence can be achieved through performing retro-commissioning follow up, particularly when significant increases are observed in metered energy consumption data, but also at other times as retro-commissioning procedures and technology continually improve.

In preliminary building energy assessments, it is often desired to estimate a motor's input power. Motor power estimates in this context should be rapid, safe, and noninvasive. Existing methods for motor input power estimation, such as direct measurement (wattmeter), Current Method, and Slip Method were evaluated. If installed equipment displays input power or average current, then using such readings are preferred. If installed equipment does not display input power or current, the application of wattmeters or current clamps is too time-consuming and invasive for the preliminary energy audit. In that case, if a shaft speed measurement is readily available, then the Slip Method is a satisfactory method for estimating motor input power. An analysis of performance data for 459 motors suggests comparable performance for predicting normalized (to the nominal motor input power) motor input power with the Current and Slip Methods: 10.0% and 9.9% RMSE, respectively. Both of these methods may be improved by applying regression on the predicted variable and/or nameplate parameters. For example, the Slip Method could be improved by applying a second-order regression, thereby reducing the predicted load factor residual RMSE of the data set from 9.0% to 8.2%. The Current and Slip Methods were also evaluated on two real motors. The normalized (to the nominal motor input power) predicted input power RMSE for the Current Method was on average 15% for the two motors; for the Slip Method the corresponding average was 17.5%. In some cases, shaft speed measurements may not be available. A temperature-based approach for estimating motor input power was investigated. Other required parameters include ambient temperature, motor efficiency, and a motor thermal constant. The temperature approach offers quick, safe, and non-invasive motor power estimation. However, thermal coefficients may vary significantly across motors and a model to predict the thermal coefficients has yet to be developed. Furthermore, the temperature approach has a very strong dependence on motor efficiency uncertainty. Experiments were performed on two motors to determine their motor thermal constants. If a motor's thermal constants and running efficiency are known, then this method gave motor input power estimates with a RMSE (normalized to the nominal input power) on the order of 4% for the studied motors.

Re-commissioning, retro-commissioning, Continuous Commissioning® (CC®) are examples of successful systematic processes implemented in buildings to reduce overall building energy consumption, and improve efficiency of systems and their operations and control. The impact of the Continuous Commissioning® Process on the Energy Star® Rating (ESR) of office buildings and hospitals is examined in this thesis. The improvement in performance of a building, and subsequently its ESR, is found to be influenced by its initial ESR, while its location has no impact on improvement. The improvement in ESR is observed to be almost linearly proportional to the percentage of energy saved. For 10% - 20% reductions in energy use typical of the CC® process, the ESR is increased by 10-19 ESR ranks for office buildings and by 13 - 26 ESR ranks for hospitals. The CC® process is found to potentially enable an office building of average initial ESR of 62 and a hospital of average initial ESR of 55, located anywhere in the US, to be eligible to achieve ESR of 75 and consequently the Energy Star recognition. The improvement of ESR is a function of the initial ESR and the building type; hence it is observed to be different for hospitals and office buildings in the study. For hospital and office building models occupying 100,000 ft² of floor area each, a difference of about 30% in the ESR improvement (greater for hospitals) is observed. The energy intensities may be different for buildings with same ESRs that have different location and/or type. An averaged maximum difference of energy intensity of approximately 10% is observed to exist for identical buildings and of the same type but located at different locations. Hospitals are observed to be more than twice as energy intensive as office buildings for the same location and equal ESRs. ESR plotted against % energy savings at site reveals the stepped nature of ESR system. At specific initial ESR and corresponding % savings a reduction of up to approximately 1% for office buildings and up to 1.5% for hospitals does not change the respective ESRs for the model set of buildings in the study.