Measured energy savings promote and sustain energy conservation retrofits by verifying the success of retrofits, determining pay-back schedules, guiding the selection of future retrofits and identifying opportunities for further savings. This dissertation develops a methodology to measure retrofit energy savings and the uncertainty of the savings in commercial buildings. The functional forms of empirical models of cooling and heating energy use in commercial buildings are derived from an engineering analysis of constant-air-volume and variable-air-volume HVAC systems. One, two, three and four parameter, temperature-dependent regression models are proposed to model baseline energy use. Retrofit savings are measured as the difference between the baseline energy use projected by the models and the measured post-retrofit energy use. A hybrid ordinary least squares/autoregressive method is developed to determine the uncertainty of the predicted energy use and savings. The annual predictive ability of models based on pre-retrofit data sets of less than a full year is investigated. The energy delivery efficiency is introduced to measure the efficiency of air-side systems at meeting the net building load. A preliminary investigation of the use of artificial neural network models to measure savings is presented. The methodology is demonstrated on case study examples using software specifically developed for the analysis of commercial building energy use.