The need for energy efficient and intelligent systemic solutions, has lead many researchers around the world to investigate and evaluate the existing technologies in order to create solutions that would be adopted for the near future intelligent homes and buildings, aiding in smart grid initiatives. In this paper an algorithm based on the adaptable learning system principles is presented. The proposed algorithm utilizes the adaptable learning system concepts. The Observe, Learn, and Adapt (OLA) algorithm proposed is the result of integration of wireless sensors and artificial intelligence concepts towards the same objective: adding more intelligence to a programmable communicating thermostat (PCT), for better energy management and conservation in smart homes. A house simulator was developed and used as an “expert system shell” to assist in implementation and verification of the OLA algorithm. The role of PCT is to provide consumer with a means to manage and reduce energy use, while accommodating their every day schedules, preferences and needs. In this paper, the actual results of learning and adaptability of a PCT equipped with OLA, as a result of the occupant's pattern/schedule changes, and in general, the overall system improvements with respect to energy consumption and savings are demonstrated via simulation for the zone controlled home equipped with OLA and Knowledge Base, versus a home without zone control, Knowledge Base nor OLA.