Industrial facilities account for approximately a third of energy usage in the world, and effective energy assessments of these facilities require a reliable baseline energy model. Commercial and residential buildings have been baselined with both simple change-point models and models that are more complex, such as Gaussian process and artificial neural networks, and these models are developed and tested with dense high-frequency data. However, industrial facilities have only been baselined using change-point models, and data for the models are typically restricted to monthly utility bills and, therefore, generally sparse data. This investigation compares the effectiveness of change-point models with that of Gaussian process models for baselining industrial facilities using only monthly utility billing information as data. Two case studies are presented to predict electricity usage and two case studies are presented to predict natural gas usage. Both change point and Gaussian process models provided similar results, and both models meet the recommended NMBE and CV-RMSE from the ASHRAE Guideline 14. For one case study, both change-point and Gaussian process models were applied using available test data not contained in the training data, and both models predicted the monthly energy usage within 10% for 4 of the 5 months of testing data used.