International audienceThis paper presents a distributed Model Predictive Control framework based on a primal decomposition and a bundle method to control the indoor environmental conditions in a multisource/multizone building. The control aims to minimize the total energy cost under restrictions on global power consumption and local constraints on comfort and saturations on actuators. Moreover, each power source is supposed to have a time varying tarification. The distributed Model Predictive Control algorithm is based on two layers: a zone layer which is responsible of local zone decisions and a coordination layer that handles decisions that go beyond the scope of the zone. Simulation results are finally provided for a three zones building with a local power production and a changing price grid power. A computational study is also provided in order to assess the effectiveness and the real-time implementability of the proposed control method. (15 References)
In this paper, the problem of minimizing energy consumption of a building zone under pre-assigned multi-variable comfort conditions and changing energy rate is addressed. The solution involves the use of a parameterized multi-variable Nonlinear Model Predictive Control (NMPC) that manages the actuation of heating/cooling, ventilation, lighting and blinds devices. Simulations of the resulting closed-loop in winter and summer seasons under varying rate profile are proposed to assess its efficiency. Moreover, a sensitivity analysis is conducted to show how the comfort level assignment impacts the level of energy consumption.
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