Predictive control of floor radiant heating system via fuzzy logic and particle swarm optimization for reducing room temperature fluctuations

Abstract

ABSTRACTMost of the activities in human life are in buildings, and suitable room temperature control is helpful for energy consumption, cost savings, and thermal comfort. A TRNSYS simulation model of floor radiant heating system for a typical 100 m2 building in the cold region of the northern China was developed, and fuzzy logic control table was determined to develop predictive control on buildings. According to the differences between room temperature and setting temperature, room temperature change, valve operation time was predicted in the control cycle. The control table was optimized by particle swarm optimization and the best control table was obtained to reduce room temperature fluctuations. Control period was studied that comparing control period of 1 h, 0.5 h control period can reduce cumulative-unsatisfied time by 83.1%. Furthermore, the control performance impacts of room temperature, water temperature, insulation thicknesses of exterior wall, thermal inertia index of exterior wall and thicknesses of floor filling layer on room temperature fluctuations under this method were studied. Among them, the floor filling layer heat storage capacity of the building envelope plays a great important role which floor filling layer thickness from 60 mm to 100 mm is increased cumulative-unsatisfied time by 129.6%. The study results show that the room temperature can be maintained in the range of room setting temperature ±0.5°C by combining fuzzy logic with particle swarm optimization under the above influencing factors. The particle swarm optimization is combined with fuzzy logic to develop a predictive control strategy, which is stable and reliable, and has high control precision.