A data-driven method for demand response baselining and strategy evaluation is presented. Using meter and weather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be used for evaluating demand response strategies in real-time, without having to learn complex models of the building. The methods have been integrated in an open-source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager by advising on which control actions should be during a demand response event. We provide a case study using data from a large commercial vistural test-bed building to evaluate the performance of the DR-Advisor tool. Keywords: demand response, regression trees, machine learning