Development of a model-based Predictive Controller for a heat distribution network

Research studies concerning heating and cooling systems have increased in recent years, pledging great potential for energy saving, efficient thermal energy distribution and renewable energy source integration. Currently, heating systems are managed on the basis of operator experience or by using adaptive controllers, however these solutions are not suitable when there are remarkable variations in boundary conditions (e.g. weather changes). In this context, Model Predictive Control is a promising strategy as it optimizes the control action based on the prediction of the future behavior of both system dynamics and disturbances by means of simplified models. This paper presents the control of a building heating system through a Model-in-the-Loop Model Predictive Control approach. A detailed model that replicates the behavior of the real system is controlled with a predictive controller based on a novel Dynamic Programming optimization algorithm implemented in Matlab ® . The performance of the innovative controller is compared to the results obtained with a PID controller. Overall, the Model Predictive Control strategy is able to fulfill comfort requirements properly while minimizing energy consumption.