For homes to become active participants in a smart electric grid, intelligent control algorithms are needed to facilitate autonomous interactions that take homeowner preferences into consideration. Many control algorithms for demand response have been proposed in the literature. Comparing the performance of these algorithms has been difficult because each algorithm makes different assumptions or considers different scenarios, i.e., peak load reduction or minimizing cost in response to the variable price of electricity. This work proposes a novel, flexible assessment framework using the Analytical Hierarchy Process to compare and rank residential energy management control algorithms. The framework is a hybrid mechanism that derives a ranking from a combination of subjective user input representing preferences, and objective data from the performance of the control algorithms related to energy consumption, cost, and comfort. A new algorithm was developed to map objective performance data to the Analytical Hierarchy Process's fundamental scale and form a matrix of pairwise comparisons. The assessment framework results in a single overall score for each control algorithm that can be used to rank the alternatives. The approach is illustrated by applying the assessment process to six residential energy management control algorithms. Published by Elsevier B.V.