实时电价下多联机系统运行优化策略及仿真研究*
摘要：
多联机(VRF)系统在公共建筑中应用广泛,为提高其能效,现有研究多集中于设备侧优化,针对用户侧优化的方案较少。本文基于数据驱动预测控制,综合考虑能耗、电费与舒适性的动态平衡,提出了一种面向室内设定温度的节能策略:通过训练LightGBM模型,预测系统在一定时域内的能耗及室内平均温度;采用差分进化算法求解以能耗与室内温升最小化为目标的优化问题,获得最优设定温度;基于Python-EnergyPlus建立了VRF系统仿真平台,验证并分析了不同温升惩罚系数的节能策略。结果表明,模型能准确预测能耗及室内平均温度,与设定温度固定为25 ℃相比,所提策略最高节能7.49%,节省电费8.22%。
Abstract：
Variable refrigerant flow (VRF) systems are widely utilized in public buildings. To enhance their energy efficiency, existing studies predominantly focus on equipment-side optimization, with fewer strategies targeting user-side optimization. This study proposes an energy-saving strategy for indoor set temperature based on data-driven predictive control, considering the dynamic balance among energy consumption, electricity costs, and thermal comfort. A LightGBM model is trained to predict the system’s energy consumption and average indoor temperature over a specific time horizon. The optimization problem, aiming to minimize energy consumption and indoor temperature rise, is solved using the differential evolution algorithm to determine the optimal set temperature. A VRF simulation platform is developed using Python and EnergyPlus to validate and analyse the energy-saving strategies under different temperature rise penalty coefficients. The results show that the model accurately predicts energy consumption and average indoor temperature. Compared with maintaining a fixed set temperature of 25 ℃, the proposed strategy achieves up to 7.49% energy savings and 8.22% electricity cost savings.
Keywords:real-time electricity price; VRF system; co-simulation; data-driven method; operational strategy; average indoor temperature