Design and experimental evaluation of model predictive control vs. intelligent methods for domestic heating systems

Abstract In this paper, it has been attempted to present a temperature control method for the building and, simultaneously, reduce costs of providing energy in hybrid heating systems. In the present work, a building in Tehran city has been investigated as a sample during a single day and applying intelligent control methods in the presence of two gas and solar heat sources. Furthermore, the influence of each of these methods has been studied on reducing costs as well as regulating indoor temperature. In the next step, the utilized controllers have been redesigned for the laboratory model and their capability has been evaluated through experimental tests. Based on the acquired results, it is deduced that, compared to other methods, utilizing a PID controller optimized by genetic algorithm not only reduces 50% of energy providing costs, but also regulates the inner temperature of the Lab model with an error lower than 1%. This method is faster than the others in regulating the model temperature. However, in the real building modeling case, the efficiency of this method has been reduced relative to the laboratory model, mostly because of variable conditions such as variable solar emission intensity during different hours of the day. Yet, acceptable results have been acquired by performing MPC and correct modeling. These results show that, as the variable parameters increase, the MPC presents higher capability compared to other methods In this case, the MPC has similar costs to the genetic algorithm while it regulates the temperature faster and with lower error.