Comfort Temperature Control of Heating Office Building System With Predictive Control Algorithms

The building sector consumes a substantial portion of the world's energy, surpassing other sectors. In the non-residential area, heating, ventilation, and air conditioning systems contribute the predominant share of energy demand, with heating being the foremost consumption item. This paper assesses the design and simulation of two predictive control algorithms aimed at regulating the indoor temperature of an office building to a desired level. The aim is to maintain indoor temperature at a comfortable temperature while simultaneously minimizing energy consumption during the working period. The first algorithm employed is adaptive generalized predictive control, integrated into an adaptive framework that encompasses system identification and a real-time predictive controller for parameters. The second algorithm is predictive functional control, involving the utilization of a discretized model of the process. Both control methods fall under the category of advanced control, capitalizing on predictive insights into the future behavior of the heating system, considering external temperature and potential perturbations. The simulation results in the paper utilize the SIMBAD Matlab toolbox, revealing a notable characteristic of the control strategies: its ability to achieve a balance between robustness and performance, even in the presence of environmental disturbances.