This thesis compares the variability of commissioning savings and the persistence of savings from the Normalized Annual Consumption (NAC) and standard International Performance Measurement and Verification Protocol (IPMVP) weather normalization approaches and from Option C and Option D of the IPMVP. Twenty-nine different weather years were used to obtain a set of savings results under each method. Variability of savings was quantified by the average standard deviation of the 29 percent savings results across all post-commissioning periods for each method. For the combined chilled and hot water savings, the average standard deviation is 0.39% savings for Option D using the NAC weather normalization approach, 0.57% savings for Option D using the standard IPMVP weather normalization approach, 0.71% savings for Option C with regression models using the NAC weather normalization approach, and 0.98% savings for Option C with regression models using the standard IPMVP weather normalization approach. The variability of savings persistence results deviate a little from variability of savings results. For the combined chilled and hot water persistence of savings, the average standard deviation across all post-commissioning periods is 0.48% persistence for Option D using the NAC weather normalization approach, 0.55% persistence for Option D using the standard IPMVP weather normalization approach, 0.52% persistence for Option C with regression models using the NAC weather normalization approach, and 1.26% persistence for Option C with regression models using the standard IPMVP weather normalization approach. Overall, the NAC weather normalization approach shows less variability in savings and persistence than the standard IPMVP weather normalization approach. Additionally, Option D of the IPMVP generally shows less variability in savings and persistence of savings than Option C with regression models. This thesis also determines the savings and persistence of savings from commissioning for three Texas A&M University buildings. Aggregate site savings averaged 11.4%, 16.5%, and 19.0% for the three buildings over differing periods of available data. Persistence results for the three buildings are quite favorable, as each building shows an increase in aggregate site savings between the first and last post-commissioning periods. Follow-up commissioning restored and prevented degradation of savings in two of the buildings.

The purpose of this research is to determine the average energy intensity of multi-unit residential buildings (MURBs) in Toronto, and evaluate whether certain building characteristics influence energy intensity. This information is particularly important in the Toronto market. Relative to the city’s population, Toronto has an unusually high proportion of MURBs with more than half of residential dwellings in apartment buildings. Additionally, Toronto MURBs are significant consumers of energy and produce an estimated 1.3M tonnes of CO2e each year. The ultimate goal is to assess the most efficient building retrofit measures. Energy consumption data for Toronto MURBs were collected and weather normalized. Correlations between the energy data and the building characteristics were examined. Window characteristics and heating system type were found to have the most significant influence on energy intensity. Establishing energy consumption characteristics of MURBs is the first step towards improving the energy efficiency of Toronto’s MURBs stock.

The purpose of this research is to determine the average energy intensity of multi-unit residential buildings (MURBs) in Toronto, and evaluate whether certain building characteristics influence energy intensity. This information is particularly important in the Toronto market. Relative to the city’s population, Toronto has an unusually high proportion of MURBs with more than half of residential dwellings in apartment buildings. Additionally, Toronto MURBs are significant consumers of energy and produce an estimated 1.3M tonnes of CO2e each year. The ultimate goal is to assess the most efficient building retrofit measures. Energy consumption data for Toronto MURBs were collected and weather normalized. Correlations between the energy data and the building characteristics were examined. Window characteristics and heating system type were found to have the most significant influence on energy intensity. Establishing energy consumption characteristics of MURBs is the first step towards improving the energy efficiency of Toronto’s MURBs stock.