An algorithm was developed to automate the process of selecting a temperature dependent change point model. Regression models based solely on outdoor air temperature for monitoring and verification purposes are common. The correct change point model shape is determined through a series of three tests. The first test checks whether the coefficients of the model are the correct sign for the shape. The second test checks if the coefficients for the model are significant. The final test checks whether enough data points are present in each temperature region of the model. The algorithm was tested with synthetic EnergyPlus electricity and natural gas data for an outpatient hospital, medium office building, large office building, large hotel, secondary school, and warehouse, with weather data from Chicago, Miami, Seattle, and Fairbanks. The algorithm was able to select the most appropriate temperature dependent change point model for all 48 cases tested. The algorithm can be used in an automated energy modeling routine for monitoring and verification or for checking human decision-making in the energy modeling process. (C) 2014 Elsevier B.V. All rights reserved.