Adaptive model predictive climate control of multi-unit buildings using weather forecast data

Abstract

Energy use in buildings contributes a large part in global energy demand. To reduce energy use in this group of consumers, specially in cold seasons, an automatic control technique is proposed. In this paper, a model predictive controller (MPC) is employed to minimize the boiler activation time. The method uses the building model and incorporates the weather forecast data to act on the actuator in an optimal fashion while treating the user comfort constraints. This technique, as a part, can be embedded into the building energy management system. The building model parameters are obtained via an online identification process using unscented kalman filter (UKF). This identification is performed on-the-fly so the model of a building is continuously updated. The results of the system identification as well as the control performance are shown via Monte Carlo simulations, and compared with the results of a conventional control law. The comparison shows that the proposed method saves %13 energy consumption in the boiler activation.