The majority of intelligent approaches supporting advanced energy-saving policies require a learning phase. Preliminary and necessary knowledge is acquired to allow the smart building management system to provide efficient energy saving services. But all those approaches are non-temporal. Decisions results from energy saving artificial intelligent algorithms run out at the smart building without examining the effects that may result from the implementation of these decisions. The series implementation of those services can lead to deficient energysaving policy, even if those decisions save some wasted energy in current situations. In this paper, we present a new approach for saving energy in smart buildings based on temporal context called TempCSESB (temporal context for Saving energy in the smart building). TempCSESB uses a new technique to enhance the effectiveness of energy-saving decisions. Before applying those decisions in the real smart building, the proposed method uses temporal context to test provided services in temporal phase. This temporal context model contains a full description of the current state of the smart building to simulate the building situations in a temporal context. The proposed approach allows knowing the effect of the services before applying them to the real smart building.



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