Energy efficiency in the building industry is related to the amount of energy that can be saved through thermal improvement. Therefore, it is important to determine the energy saving potential of the buildings to be thermally upgraded in order to check whether the set targets for the amount of energy saved will be reached after the implementation of corrective measures. In real residential buildings, when starting to make energy calculations, one can often encounter the problem of incomplete architectural documentation and inaccurate data characterizing the object in terms of thermal (thermal resistance of partitions) and usable (number of inhabitants). Therefore, there is a need to search for methods that will be suitable for quick technical analysis of measures taken to improve energy efficiency in existing buildings. The aim of this work was to test the usefulness of the type Takagi-Sugeno fuzzy models of inference model for predicting the energy efficiency of actual residential buildings that have undergone thermal improvement. For the group of 109 buildings a specific set of important variables characterizing the examined objects was identified. The quality of the prediction models developed for various combinations of input variables has been evaluated using, among other things, statistical calibration standards developed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The obtained results were compared with other prediction models (based on the same input data sets) using artificial neural networks and rough sets theory.