The reduction of energy consumption in buildings is one of the goals to improve energy efficiency. One way to achieve energy savings in buildings is to develop intelligent control heating strategies that can reduce the power consumption by predicting the behavior of the thermal dynamics under different control schemes. One way to accomplish this is by means of learning fuzzy rules using the data collected from different sensors installed in buildings to generate regression models that are accurate and interpretable, so the generated models can be understood by the experts who approve the energy saving schemes. However, one critical issue is the generation of accurate knowledge bases of fuzzy rules for regression that can scale with the large amount of information generated by the many sensors installed in buildings, which will continue to grow in the coming years. For this purpose, in this paper we evaluate the scalability of two genetic fuzzy systems, FRULER and S-FRULER in the domain of thermal dynamics in buildings, using real data from a residential college at the USC.