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An effective way to reduce heating cost of building is to utilize the building thermal mass as a renewable energy source. In particular, floor heating (FH) system is superior in using building thermal mass to switch heating energy demand to off-peak hours. However, thermal environment control of room integrated with floor heating system is a challenging problem, due to the complex dynamic characteristics of indoor thermal environment and the large thermal inertia of floor heating systems. Variation of outdoor climate and occupancy also adds complexity to the control. In this paper, a model predictive controller is proposed, which allows the indoor thermal comfort to be maintained within the desired range, taking into account external influencing variables such as outdoor climate, occupancy and dynamic energy price. Considering the accuracy of prediction model and the computational burden, the predictive model is obtained in the form of a statistic state space. Different control strategies are tested and compared in terms of energy consumption, thermal comfort and operating cost using TRNSYS-MATLAB integrated simulation environment. Simulation results show that the proposed method improves indoor thermal comfort and reduces energy demand during peak load using building thermal mass.