Operation optimization of ventilated floor heating system in nearly-zero-energy building

As the share of renewable energy sources increases and building energy systems become more complex, optimizing system operation is necessary to fully improve economic, environmental, renewable penetration and thermal comfort performance. Most current studies focus on single-objective or multi-objective optimization of building systems, but the impact of different objectives and their combinations on operation is unclear. In this study, optimal schedules for a ventilated heating floor in a nearly-zero-energy building were determined using an improved particle swarm optimization algorithm coupled with prior knowledge-based search accelerating strategy. System performance was investigated under different combinations of optimization objectives, including reducing operational costs and carbon emissions, increasing wind power penetration, and improving thermal comfort. The results show that under the influence of prior knowledge, stochastic algorithm converges quickly with reasonable results even when the solution space is large. The performance of some single-objective optimization cases is unsatisfactory due to the exclusion of some control variables. In contrast, multi-objective optimization yields optimization problems better, which finds better dynamic operation status and leads to better system performance. Specifically, co-optimization of operational cost, wind power penetration and environmental cost together produces the most reasonable solution.